diff --git a/2003.06658/main_diagram/main_diagram.drawio b/2003.06658/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..aae81c9247b3b8e4c243bfc83288cd33f3c40ec8
--- /dev/null
+++ b/2003.06658/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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
\ No newline at end of file
diff --git a/2003.06658/main_diagram/main_diagram.pdf b/2003.06658/main_diagram/main_diagram.pdf
new file mode 100644
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Binary files /dev/null and b/2003.06658/main_diagram/main_diagram.pdf differ
diff --git a/2003.06658/paper_text/intro_method.md b/2003.06658/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..8dad2349c17d8ea042de09aa6182402bffd53fd1
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+++ b/2003.06658/paper_text/intro_method.md
@@ -0,0 +1,68 @@
+# Introduction
+
+As a crucial characteristic of human cognition, systematic generalization reflects people's ability to learn infinite combinations of finite concepts [@chomsky1957syntactic; @montague1970universal]. However, weak systematic compositionality has been considered as a primary obstacle to the expression of language and thought in connectionist networks for a long time [@fodor1988connectionism; @hadley1994systematicity; @marcus1998rethinking; @fodor2002compositionality; @frank2009connectionist; @brakel2009strong; @marcus2018algebraic]. Whether models can generalize systematically is still an appealing research topic until now. Recent works state that modern neural networks have not mastered these language-based generalization challenges in multiple explicitly proposed datasets [@lake2017generalization; @bastings-etal-2018-jump; @keysers2019measuring; @hupkes2020compositionality; @kim2020cogs]. These studies conclude that models lack such cognitive capacity, which calls for a more systematic study. Apart from the proposal of benchmarks, existing research mainly focuses on novel architectural designs [@ChenLYSZ20] or meta-learning [@NEURIPS2019_f4d0e2e7; @conklin-etal-2021-meta] to enable systematic generalization.
+
+In this work, however, we question that whether neural networks are indeed deficient or just conventional learning protocols unable to exploit their full potential [@csordas2021devil]. Inspired by *meaningful learning* from the field of educational psychology [@mayer2002rote], we revisit systematic generalization and explore *semantic linking*. Specifically, we propose augmenting prior knowledge to build relation links between new concepts and existing ones through either *inductive learning* or *deductive learning* as what humans do in meaningful verbal learning [@ausubel1963psychology]. To elaborate, inductive learning is a bottom-up approach from the more specific to the mode general. By introducing new concepts sharing the same context with existing ones in specific samples, we hope the model can capture the underlying semantic connections and thus generalize to novel compositions of new concepts. On the contrary, deductive learning is a top-down approach from the mode general to the more specific. By involving a rule-like concept dictionary without specific context information, we hope the model can utilize the general cross-lingual supervised signals as anchor points so as to launch the semantic linking. We mainly focus on three semantic relationships, namely, lexical variant, co-hyponym, and synonym.
+
+Starting from SCAN, our experiments confirm that, with semantic linking, even canonical neural networks can significantly improve its systematic generalization capability. Moreover, this holds consistent across two more semantic parsing datasets. As an ablation study, we further examine such one-shot compositional generalization and find that both prior knowledge and semantic linking take essential parts. Lastly, we extend from toy sets to real data and explain how semantic linking, as data augmentation techniques, benefits models' performance in solving real problems such as machine translation and semantic parsing.
+
+Overall, our contributions are as follows: ($1$) We formally introduce semantic linking for systematic generalization through the analysis of inductive and deductive learning from a meaningful learning perspective. ($2$) We observe that modern neural networks can achieve systematic generalization with semantic linking. ($3$) We show that both prior knowledge and semantic linking play a key role in systematic generalization, which is in line with meaningful learning theory. ($4$) We extend from SCAN to real data and demonstrate that many recent data augmentation methods belong to either inductive learning or deductive learning.
+
+
+
+
+
+An illustration of the semantic linking injection pipeline in SCAN. Models are expected to generalize to new compositions of variants after augmenting the prior knowledge through either inductive learning or deductive learning.
+
+
+Learning new concepts by relating them to the existing ones is defined as a process of meaningful learning in educational psychology [@ausubel1963psychology; @mayer2002rote]. The utilization of meaningful learning can encourage learners to understand information continuously built on concepts the learners already understand [@okebukola1988cognitive]. Following the same idea, we intend to examine models' systematic compositionality by exploring semantic linking, an augmentation that establishes semantic relations between primitives $\sP$ (old concepts) and their variants $\sV:=\{\sV_{\evp} ~|~ \forall \evp \in \sP\}$ (new concepts). To spoon-feed semantic knowledge to models for semantic linking, we propose to augment the training data by either inductive learning or deductive learning [@hammerly1975deduction; @shaffer1989comparison; @thornbury1999teach]. In this section, we discuss the definition of semantic linking and take "jump\" from SCAN as an example primitive to illustrate the learning scheme in Figure [1](#fig:pipeline){reference-type="ref" reference="fig:pipeline"}.
+
+We aim to achieve systematic generalization by exposing semantic links such as lexical variants, co-hyponyms, and synonyms. *Lexical Variant* refers to an alternative expression form for the same concept. *Co-hyponym* is a linguistic term to designate a semantic relation between two group members belonging to the same broader class, where each member is a hyponym and the class is a hypernym [@lyons1995linguistic]. *Synonym* stands for a word, morpheme, or phrase that shares exactly or nearly the same semantics with another one. We provide an example and a detailed description in Appendix.
+
+Inductive learning is a bottom-up approach from the more specific to the more general. For example, fitting a machine learning model is a process of induction, where the model itself is the hypothesis that best fits the observed training data [@Mitchell97]. In grammar teaching, inductive learning is a rule-discovery approach that starts with the presentation of specific examples from which a general rule can be inferred [@thornbury1999teach]. Inspired by that, we propose to augment data inductively by introducing variants sharing the same context with their primitives in specific samples. The assumption is that models can observe the interchange of primitives and their variants surrounded by the same context in the hope of coming up with a general hypothesis that there is a semantic linking between primitives and their variants [@harris1954distributional]. Formally, we describe inductive learning as follows. For a sequence-to-sequence task $\mathcal{T} : \mX \rightarrow \mY$, we have a source sequence $\vx \in \mX$ and its target sequence $\vy \in \mY$. We prepare prompts set $\mZ := \{ \vz = f_{prompt}(\vx) ~|~ \vx \in \mX\}$, where $f_{prompt}(\cdot)$ replaces the primitive in $\vx$ with a slot mark $[z_{p}]$ as in Figure [1](#fig:pipeline){reference-type="ref" reference="fig:pipeline"}.[^2] Then, we generate $\mX^{IL} := \{ \vx^{IL} = f_{fill}(\vz, \evv) ~|~ \vz \in \mZ, \evv \in \sV \}$ by filling $[z_{\evp}]$ with variants in $\sV_{\evp}$. There is no change from the target side, so we get $\mY^{IL}$ by copying $\vy$ as $\vy^{IL}$ for each $\vx^{IL}$ correspondingly. Finally, we train models on $(
+\big[\begin{smallmatrix}
+\mX \\
+\mX^{IL}
+\end{smallmatrix}\big]
+,
+\big[\begin{smallmatrix}
+\mY \\
+\mY^{IL}
+\end{smallmatrix}\big]
+)$ to operate semantic linking inductively.
+
+Deductive Learning, on the opposite of inductive learning, is a top-down approach from the more general to the more specific. As a rule-driven approach, teaching in a deductive manner often begins with presenting a general rule and is followed by specific examples in practice where the rule is applied [@thornbury1999teach]. To align with this definition, we intend to augment data deductively by combining a bilingual dictionary that maps primitives and their variants to the same in the target domain. This additional dictionary, hence, mixes the original training task with word translation [@mikolov2013exploiting]. Without any specific context, we hope the model can utilize the general cross-lingual supervised signals as anchor points so as to launch the semantic linking. Formally, we describe deductive learning as follows. We first treat $\sP$ as the source dataset $\mX^{DL}_{\sP}$ directly and then prepare the corresponding target dataset $\mY^{DL}_{\sP}$ by either decomposing samples from $\mY$ manually or feeding $\mX^{DL}_{\sP}$ to a trained external model. Similarly, we can consider $\sV$ as another source dataset $\mX^{DL}_{\sV}$ and prepare its target dataset $\mY^{DL}_{\sV}$ by copying the corresponding $\vy^{DL}_{\sP}$ as $\vy^{DL}_{\sV}$ for all $\vx^{DL}_{\sV}$ as variants of each $\vx^{DL}_{\sP}$. After all, we get $\mX^{DL}$ as $\big[\begin{smallmatrix}
+\mX^{DL}_{\sP} \\
+\mX^{DL}_{\sV}
+\end{smallmatrix}\big]$ and $\mY^{DL}$ as $\big[\begin{smallmatrix}
+\mY^{DL}_{\sP} \\
+\mY^{DL}_{\sV}
+\end{smallmatrix}\big]$. The mapping from $\mX^{DL}$ to $\mY^{DL}$ is a dictionary to translate primitives and their variants to the same targets without any specific context information. We name $(\vx^{DL}, \vy^{DL})$ as a *concept rule*, $(\vx^{DL}_{\sP}, \vy^{DL}_{\sP})$ as a *primitive rule*, and $(\vx^{DL}_{\sV}, \vy^{DL}_{\sV})$ as a *variant rule* since they are more rule-like without contexts. We train models on $(
+\big[\begin{smallmatrix}
+\mX \\
+\mX^{DL}
+\end{smallmatrix}\big]
+,
+\big[\begin{smallmatrix}
+\mY \\
+\mY^{DL}
+\end{smallmatrix}\big]
+)$ to operate semantic linking deductively.
+
+Although previous studies argue that neural networks fail to match humans in systematic generalization [@lake2017generalization; @keysers2019measuring], we revisit such algebraic compositionality conditioned on the semantic linking to see whether the conclusion will change. The following section moves on to specify the process and outcome of experiments. We first intend to make use of SCAN as the initial testbed to observe the presence of systematic generalization with the assistance of semantic relations. Then, we verify neural networks' potential to achieve the systematic generalization activated by semantic linking on SCAN, as well as two real-world tasks of semantic parsing. Following ablation studies further examine models' compositional capability.
+
+There is evidence suggesting that SCAN may be far from enough to fully capture the kind of generalization, where even a simple model can behave as if it owns comparable skills [@bastings-etal-2018-jump; @keysers2019measuring]. Thus, starting from SCAN, we introduce GEO and ADV generated respectively from real semantic parsing datasets: Geography and Advising.[^3] Modification on datasets is specified in each experiment for the goal of examining machines' systematic generalization across various conditions.
+
+**SCAN** is one of the benchmarks to investigate neural networks' compositional generalization [@lake2017generalization]. It includes 20910 pairs of commands in English to their instructed action sequences [^4]. We define $\sP^{SCAN} := \{ ``\textit{jump}", ``\textit{look}", ``\textit{run}", ``\textit{walk}" \}$ to be in line with previous works. We focus on lexical variants and create $\sV^{SCAN}$ by adding a suffix that consists of an underline and a unique number for each primitive. We control $|\sV^{SCAN}|$ by setting the upper limit of this number. An example variant of "*jump*" is "*jump_0*\" and both mean the same action "JUMP\".
+
+**Geography** is a common semantic parsing dataset [@data-geography-original; @data-atis-geography-scholar]. It is also named as *geo880* since it contains 880 examples of queries about US geography in natural language paired with corresponding query expressions. It is later formatted to SQL language with variables in the target sequences [@data-sql-advising]. **GEO** is the dataset generated based on Geography, where we regard 4 of 9 annotated variables as hypernyms and keep them as they are in SQL sequences. The other variables are restored by entities from the source sequence accordingly. As a result, the overall data size is 618 after processing and we can make use of the "is-a\" hypernymy relations between entities and variables for semantic linking. To be specific, we define $\sP^{GEO} :=\{``\textit{new york city}", ``\textit{mississippi rivier}", ``\textit{dc}", ``\textit{dover}" \}$ with $\sV^{GEO}$ consisting of entities as co-hyponyms sharing the same variable group with primitives.[^5] An example variant of "*new york city*" is "*houston city*\" and both are in the same variable group "CITY_NAME".
+
+**Advising**, as our second semantic parsing dataset, includes 4570 questions about course information in natural language paired with queries in SQL [@data-sql-advising]. Similar to GEO, **ADV** is generated on the basis of Advising with 4 of 26 variables as hypernyms. Precisely, we define $\sP^{ADV}:=\{ ``\textit{a history of american film}", ``\textit{aaron magid}", ``\textit{aaptis}", ``\textit{100}" \}$ and $\sV^{ADV}$ as co-hyponyms of primitives sharing the same variables. For instance, "*advanced at ai techniques*\" is a co-hyponym of "*a history of american film*\" sharing the same variable "TOPIC\".
+
+What follows is an account of network configurations and experimental settings. Without specific instruction, they are shared throughout experiments.
+
+**Models.** After testing a range of their adapted versions, we employ three dominant model candidates with an encoder-decoder framework [@sutskever2014sequence], that is, RNN, CNN, and TFM. In terms of RNN, we reproduce bi-directional recurrent networks [@schuster1997bidirectional] with long short-term memory units [@hochreiter1997long] and an attention mechanism [@bahdanau2014neural]. We follow the convolutional seq2seq architecture presented by @gehring2017convolutional with regard to CNN and the attention-based structure proposed by @NIPS2017_7181 in the case of TFM. More details are provided in Appendix.
+
+**Training.** We apply the mini-batch strategy to sample 128 sequence pairs for each training step. We use Adam optimizer [@DBLP:journals/corr/KingmaB14] with an $\ell_2$ gradient clipping of $5.0$ [@10.5555/3042817.3043083] and a learning rate of 1$e^{-4}$ to minimize a cross-entropy loss. We freeze the maximum training epoch at 320 for CNN and 640 for RNN and TFM. In contrast to early stopping [@prechelt1998early], we prefer a fixed training regime sufficient enough for models to fully converge in practice with a focus on the systematic generalization observation instead of superior structure exploration. To prevent uncontrolled interference, we train all models from scratch instead of fine-tuning [@devlin-etal-2019-bert].
+
+**Evaluation.** Token accuracy and sequence accuracy serve as two primary metrics in the following experiments. The former is a soft metric that allows partial errors in a sequence, while the latter is tricky and strictly does not. The reported results, along with standard deviation, are the mean of five runs.
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diff --git a/2012.05688/paper_text/intro_method.md b/2012.05688/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..00779f54804138c7bd2d275e8f53df3b79b44581
--- /dev/null
+++ b/2012.05688/paper_text/intro_method.md
@@ -0,0 +1,64 @@
+# Method
+
+Given a fully-labeled source $HIN_S=\left(\mathcal{V}_S^{l},\mathcal{E}_S, \mathcal{A}_S, \mathcal{R}_S\right)$ and a fully-unlabeled target $HIN_T=\left(\mathcal{V}_T^{u},\mathcal{E}_T, \mathcal{A}_T, \mathcal{R}_T\right)$, where $\mathcal{V}$ and $\mathcal{E}$ are node and edge set, $\mathcal{A}$ and $\mathcal{R}$ denote the sets of node and edge types. $\mathcal{A}_S$ and $\mathcal{A}_T$ not only share same node types but also have their own private node types, also leading to the difference between $\mathcal{R}_S$ and $\mathcal{R}_T$. $\mathcal{V}_S^{l}$ represents a set of labeled nodes while $\mathcal{V}_T^{u}$ represents a set of unlabeled nodes. The transferable classification aims to predict the label on $HIN_T$ with label information from $HIN_S$.
+
+To align each semantic component independently between source and target domains, we construct *pairwise node-type* auto-encoders and domain discriminators before aggregation and updating:
+
+**1) Shared Node-type Distribution Alignment:** Assuming there are $K_1$ shared node-type pairs between source and target domains, the *pairwise node-type* auto-encoder $AE^{k_1},{k_1}=1,...,K_1$ is used to encode and decode the node features of ${k_1}$-th-type nodes. A reconstruction loss is used to constrain $AE^{k_1}$'s projection retaining semantic information: $$\begin{equation}
+ \small
+\label{equa:loss_MSE}
+\mathcal{L}_{\text {recon1}} = \sum MSE\left(\mathbf{X}^{k_1},\hat{\mathbf{X}}^{k_1}\right),
+\end{equation}$$ where $\mathbf{X}^{k_1}$ is the node feature of the ${k_1}$-th shared node type for both domains, $\hat{\mathbf{X}}^{k_1}$ is the corresponding reconstructed feature, and $MSE$ represents the mean square error. Then, we construct a *pairwise node-type* domain discriminator following with a Gradient Reversal Layer (GRL) [@ganin2016domain] for the features of individual-type nodes separately. By minimizing the domain adversarial similarity loss, the encoder is trained to make similar its output processed from both domains while the domain discriminator learns to identify the domain of the encoder's output. The domain discriminator loss for all discriminators $D^{k1}$ and auto-encoders $AE^{k1}$, is formulated as: $$\begin{align}
+\label{equa:loss_nda1}
+\mathcal{L}_{nda1} = \sum \mathbb{E}_{x \in \textbf{X}_S^{k_1}} \left[\textbf{{\rm log}} \left(1-D^{k_1}\left(AE^{k_1}\left(x\right)\right)\right)\right] \nonumber \\
++\mathbb{E}_{x \in \textbf{X}_T^{k_1}}\left[\textbf{{\rm log}} D^{k_1}\left(AE^{k_1}\left(x\right)\right)\right],
+\end{align}$$ where $D^{k_1}$ is the discriminator for the ${k_1}$-th-type nodes.
+
+**2) Private Node-type Distribution Alignment:** Unlike shared-node types, private-node types contain many unknown values. Reconstruction constraints applied to private-node types recover not only the observed values but also unknown parts. Meanwhile, the private-node types like term and field are semantically relevant [@mikolov2013efficient], which makes the unobserved type's embedding contain plenty of linear dependent columns. Hence, the problem of recovery unobserved type's embedding turns into a low-rank matrix completion problem, which can be formulated to minimize the nuclear norm under the constraint of reconstruction loss [@candes2009exact]. For private node-type pairs, we recover the missing value and get a matrix $\hat{\mathbf{W}}$: $$\begin{equation}
+\label{equa:matrix_recover}
+\mathbf{W}=\left[\begin{array}{ll}
+\textbf{X}_{S} & \textbf{0} \\
+\textbf{0} & \textbf{X}_{T}
+\end{array}\right] \stackrel{recover}{\Longrightarrow}
+\hat{\mathbf{W}}=\left[\begin{array}{ll}
+\hat{\textbf{X}}_{S} & \hat{\textbf{U}}_{S} \\
+\hat{\textbf{U}}_{T} & \hat{\textbf{X}}_{T}
+\end{array}\right].
+\end{equation}$$
+
+In $\hat{\mathbf{W}}$ above, $\hat{\mathbf{X}}_S$ and $\hat{\mathbf{X}}_T$ represent the recovered observed elements, while $\hat{\mathbf{U}}_S$ and $\hat{\mathbf{U}}_T$ represent the recovered unobserved elements. To recover $\hat{\mathbf{W}}$, we minimize the loss $\mathcal{L}_{\text {recon2}}$: $$\begin{align}
+\label{equa:loss_recon2}
+%\mathcal{L}_{\text {recon2}} &=\sum_{m=1}^{M}(\mathcal{L}^m_{\text {mse}} +\delta\mathcal{R}(\hat{\mathbf{W}})), \\
+% \mathcal{L}^m_{\text {mse}} = \frac{1}{M}\sum_{m=1}^{M}\|\mathbf{X}^s_m&-\hat{\mathbf{X}}^s_m\|_{F}^{2} +\frac{1}{M}\sum_{m=1}^{M}\|\mathbf{X}^t_m-\hat{\mathbf{X}}^t_m\|_{F}^{2} ,
+\mathcal{L}_{\text {recon2}} &=\sum MSE\left(\mathbf{X}^{k_2},\hat{\mathbf{X}}^{k_2}\right) +\delta\mathcal{R}\left(\hat{\mathbf{W}}\right),
+\end{align}$$ where $\mathbf{X}^{k_2}$ is the node feature of the $k_2$-th private node type, $\hat{\mathbf{X}}^{k_2}$ is corresponding observed part of recovered embedding, $K_2$ is the number of the private-type pairs. $\mathcal{R}(\hat{\mathbf{W}})$ is a regularization term, where $\mathcal{R}(*)$ denotes nuclear norm operation, and $\delta >0$ is a trade-off parameter. Under the reconstruction constraint, the encoder's output can retain enough semantic information for the private-type pairs. The loss of discriminators for $k_1$ shared- and $k_2$ private-type pairs is $\mathcal{L}_{nda} = \mathcal{L}_{nda1} + \mathcal{L}_{nda2}$, where $\mathcal{L}_{nda2}$ is similar as Eq. ([\[equa:loss_nda1\]](#equa:loss_nda1){reference-type="ref" reference="equa:loss_nda1"}).
+
+In this subsection, we further elaborate on how to align the topological structure of source and target networks.
+
+**Representation of $\mathbf{h}$-hop:** We choose the advanced HIN model HGT [@hu2020heterogeneous] as feature extractor $G$, which can learn embeddings of $\mathbf{h}$-hop structure ($\{\mathcal{N}^1_{(v)},...,\mathcal{N}^h_{(v)}\}$), that is $\mathbf{h}$-hop embedding, to capture network topology information. In GDA-HIN, nodes from the source and target networks are encoded via a feature extractor with shared learnable parameters. However, there are still $\mathbf{h}$-hop structure discrepancies between both domains. Then, we adopt the domain alignment on the output of HGT, which aligns the data distribution in the embedding space of $\mathbf{h}$-hop structure.
+
+**Topological Domain Discriminator:** After extracting each nodes' h-hop structure embedding by feature extractor $G$, which is a 2-layer HGT, another domain adversarial discriminator $D^{tp}$ is implemented to minimize the topological structure discrepancy, and its loss $\mathcal{L}_{da}$ defined as: $$\begin{align}
+\label{equa:loss_da}
+\mathcal{L}_{da} = \mathbb{E}_{x \in \boldsymbol{H_S}}\left[\textbf{{\rm log}} \left(1-D^{tp}\left(G\left(x\right)\right)\right)\right] \\ \nonumber
++\mathbb{E}_{x \in \boldsymbol{H_T}}\left[\textbf{{\rm log}} D^{tp}\left(G\left(x\right)\right)\right],
+\end{align}$$ where $\mathbf{H}_S$ and $\mathbf{H}_T$ represent the outputs of encoders in source and target domains, respectively.
+
+**Domain-invariant Classifier:** The classifier $C$ is used to to predict the label, and its loss $\mathcal{L}_{cls}$ is defined as: $$\begin{align}
+\label{equa:loss_t_cls}
+\mathcal{L}_{cls} =-\frac{1}{N^l}\mathop{\Sigma}_ {i=0}^{N^l}{Y^i log\left(\hat{Y}^i\right)} + \zeta tr\left(\mathbf{H}^\top \mathbf{L^gH}\right),
+\end{align}$$ where $\zeta$ is a balance parameter, $N^l$ represents the number of labeled source- and target-domain nodes, and $\hat{Y}^i$ denotes the $i$-th node's prediction. The regularization term in Eq.([\[equa:loss_t_cls\]](#equa:loss_t_cls){reference-type="ref" reference="equa:loss_t_cls"}) is defined as $tr(\mathbf{H}^\top \mathbf{L^gH})$, where $\mathbf{H}$ represents the hidden state of auto-encoders for all private-type nodes and $\mathbf{L^g}$ denotes the graph Laplacian matrix for all private-type nodes. In particular, the graph Laplacian matrix is formulated as $\mathbf{L^g} = \begin{pmatrix} \mathbf{L_S} & \mathbf{0} \\ \mathbf{0} & \mathbf{L_T} \end{pmatrix}$, where $\mathbf{L_S}$ and $\mathbf{L_T}$ are the Laplacian matrices of source and target domains computed according to the adjacency matrices. In the matrix completion module, there are unobserved elements involved in the computation of private-type nodes' embeddings, which are not constrained by reconstruction loss. We utilize graph Laplacian matrix to smooth their embedding over the graph, relying on the assumption that connected nodes in the graph are likely to share the same label [@kipf2016semi].
+
+**Phase Training:** To minimize the nuclear norm under the constraint of reconstruction loss Eq.([\[equa:loss_recon2\]](#equa:loss_recon2){reference-type="ref" reference="equa:loss_recon2"}), GDA-HIN trains a *Phase Training model* on the shared node types of two networks to yeild pseudo labels, and the overall objective function is composed of the following four components: $$\begin{align}
+\label{equa:l_stage1}
+\mathcal{L}_{p1} = \mathcal{L}_{cls}+\alpha \mathcal{L}_{recon1} + \beta\mathcal{L}_{nda1} + \gamma\mathcal{L}_{da},
+\end{align}$$
+
+where $\alpha$, $\beta$ and $\gamma$ are hyper-parameters.
+
+When there are only shared node types, Eq.([\[equa:loss_t_cls\]](#equa:loss_t_cls){reference-type="ref" reference="equa:loss_t_cls"}) degenerates to cross-entropy loss, $\mathcal{L}_{cls} =-\frac{1}{N_S}\mathop{\Sigma}_ {i=0}^{N_S}{Y_S^ilog\left(\hat{Y}_S^i\right)},$ where $N_S$ denotes the number of source-domain nodes.
+
+**Phase Training:** For phase training, we select some predictions by *Phase Training model* as pseudo labels for nodes in target domain. Under the previous phase's guidance, GDA-HIN considers both shared- and private- node types from two networks, and the overall optimization objective is: $$\begin{align}
+\label{equa:l_stage2}
+\mathcal{L}_{p2} = \mathcal{L}_{cls}+\alpha \left(\mathcal{L}_{recon1}+\mathcal{L}_{recon2}\right)
++ \beta \mathcal{L}_{nda} + \gamma\mathcal{L}_{da}.
+\end{align}$$
diff --git a/2101.01000/main_diagram/main_diagram.drawio b/2101.01000/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..9a2c855f99a60740bea468a0c72a00040750dd33
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+# Introduction
+
+In classical statistical learning, spatio-temporal forecasting is usually regarded as a multi-variate time series problem, and methods such as autoregressive integrated moving average (ARIMA) with its variants are proposed, but the stationary assumption is usually hard to satisfy. Recently, the rise of deep learning approaches has attracted lots of attention. For example, in traffic flow forecasting, Graph Neural Networks (GNNs) are regarded as superior solutions to model spatial dependency by taking sensors as graph nodes [@yu2018STGCN; @li2018diffusion]. Compared with great progress in traffic forecasting, works focusing on meteorology are scarce, while the need for weather forecasting is increasing dramatically [@shi2015convolutional; @sonderby2020metnet]. In this work, our research attention is paid to spatio-temporal meteorological forecasting tasks.
+
+The task is challenging due to two main difficulties. First, the irregular sampling of meteorological signals usually disables the classical Convolutional Neural Networks (CNNs) which work well on regular mesh grid signals on Euclidean domains such as 2-D planar images. Signals are usually acquired from irregularly distributed sensors, and the manifolds from which signals are sampled are usually non-planar. For example, sensors detecting temperature are located unevenly on land and ocean, which are not fixed on structured mesh grids, and meteorological data are often spherical signals rather than planar ones. Second, the high temporal and spatial dependency makes it hard to model the dynamics. For instance, different landforms show totally distinct wind flow or temperature transferring patterns in weather forecasting tasks and extreme climate incidents like El Nino [@elnino] often cause non-stationarity for prediction.
+
+GNNs yield effective and efficient performance for irregularly spatio-temporal forecasting, enabling to update node representations by aggregating messages from their neighbors, the process of which can be analogized to heat or wind flow from localized areas on the earth surface. As discussed, the meteorological flow may demonstrate totally variant patterns in different local regions. Inspired by the analogy and location-characterized patterns, we aim to establish a graph convolution kernel, which varies in localized regions to approximate and imitate the true local meteorological patterns.
+
+Therefore, we propose our **conditional local kernel**. We embed it in a graph-convolution-based recurrent network, for spatio-temporal meteorological forecasting. The convolution is performed on the **local space** of each node, which is constructed considering both distance between nodes and their relative orientation, with the kernel proposed mainly based on the assumption: **smoothness of location-characterized patterns**. In summary, our contributions are:
+
+- Proposing a location-characterized kernel to capture and imitate the meteorological local spatial patterns in its message-passing process;
+
+- Establishing the spatio-temporal model with the proposed graph convolution which achieves state-of-the-art performance in weather forecasting tasks;
+
+- Conducting further analysis on learned local pattern visualization, framework choice, local space and map choice and ablation.
+
+# Method
+
+Given $N$ correlated signals located on the sphere manifold $S^2$ at time $t$, we can represent the signals as a (directed) graph $\mathcal{G} = (\mathcal{V}, \mathcal{E}, \bm{A})$, where $\mathcal{V}$ is a node set with $\mathcal{V} = \{\bm{\mathrm{x}}_i^S = (x_{i,1}, x_{i,2}, x_{i,3})\in S^2: i=1,2,\ldots,N\}$ meaning that it records the position of $N$ nodes, which satisfies $||\bm{\mathrm{x}}_i^S||_2 = 1$. We denote positions of nodes in Euclidean space by $\bm{\mathrm{x}}^E$, and in sphere as $\bm{\mathrm{x}}^S$. $\mathcal{E}$ is a set of edges and $\bm{A} \in \mathbb{R}^{N \times N}$ is the adjacency matrix which can be asymmetric. The signals observed at time $t$ of the nodes on $\mathcal{G}$ are denoted by $\bm{F}^{(t)} \in \mathbb{R}^{N\times D}$. For the forecasting tasks, our goal is to learn a function $P(\cdot)$ for approximating the true mapping of historical $T'$ observed signals to the future $T$ signals, that is $$\begin{align}
+ [\bm{F}^{(t-T')}, \ldots, \bm{F}^{(t)};\mathcal{G} ] \overset{P}{\longrightarrow} [\bm{F}^{(t+1)}, \ldots, \bm{F}^{(t+T)};\mathcal{G}].
+\end{align}$$ In the paper, for meteorological datasets which do not provide the adjacency matrix, we construct it by K-nearest neighbors algorithm based on induced spherical distance of their spatial location which will be discussed later.
+
+For notation simplicity, we omit the supper-script $(t)$ when discussing spatial dependency. Denote the set of neighbors of center node $i$ by $\mathcal{N}(i) = \{j:(i,j) \in \mathcal{E}\}$, and note that $(i,i) \in \mathcal{N}(i)$. In Graph Neural Networks, $(W^l, \bm{b}^l)$ is the weights and bias parameters for layer $l$, and $\sigma(\cdot)$ is a non-lieanr activation function. The message-passing rule concludes that at layer $l$, representation of node $i$ updates as $$\begin{align}
+ \bm{y}^{l}_i &= \sum_{j\in \mathcal{N}(i)}\omega_{i,j}\bm{h}^{l-1}_j; \label{eq:messgagepass}\\
+ \bm{h}^{l}_i &= \sigma(\bm{y}^{l}_i\bm{W}^l + \bm{b}^l) ,
+\end{align}$$ where $\bm{h}^{l}_i$ is the representation of node $i$ after $l$-th layer, with $\bm{h}^{0}_i = \bm{F}_i$, which is the observed graph signals on node $i$. Denote the neighborhood coordinate set of center node $i$ by $\mathcal{V}(i) = \{\bm{\mathrm{x}}^S_j: j\in \mathcal{N}(i)\}$, and then Eq. [\[eq:messgagepass\]](#eq:messgagepass){reference-type="ref" reference="eq:messgagepass"} represents aggregation of messages from neighbors, which can also be regarded as the convolution operation on graph, which can be written as $$\begin{align}
+ (\Omega \star_{\mathcal{N}(i)} \bm{H}^{l-1})(\bm{\mathrm{x}}_j^S) &= \sum_{\bm{\mathrm{x}}^S_j\in \mathcal{V}(i)} \Omega(\bm{\mathrm{x}}^S_j; \bm{\mathrm{x}}^S_i)\bm{H}^{l-1}(\bm{\mathrm{x}}^S_j), \label{eq:graphconv}
+ % &=\int_{(\phi, \theta)} \Omega(\phi,\theta) \bm{H}^{l-1}(\phi,\theta) \delta_{\mathcal{V}(i)}d\phi d\theta
+\end{align}$$ where $\star_{\mathcal{N}(i)}$ means convolution on the $i$-th node's neighborhood, $\Omega: S^2 \times S^2 \rightarrow \mathbb{R}$ is the convolution kernel, such that $\Omega(\bm{\mathrm{x}}^S_j, \bm{\mathrm{x}}^S_i) = \omega_{i,j}$, and $\bm{H}^{l-1}$ is a function mapping each point on sphere to its feature vector in $l$-th representation space.
+
+::: Example
+**Example 1**. *The convolutional kernel used in DCRNN [@li2018diffusion] is $$\begin{align}
+\Omega(\bm{\mathrm{x}}^S_j, \bm{\mathrm{x}}^S_i) =
+ &\exp(-d^2(\bm{\mathrm{x}}^S_i, \bm{\mathrm{x}}^S_j)/\tau),
+\end{align}$$ where $d(\cdot,\cdot)$ is the distance between the two nodes, and $\tau$ is a hyper-parameter to control the smoothness of the kernel.*
+:::
+
+To imitate the meteorological patterns, the value of convolution kernel should be large for neighbors having great meteorological impacts on centers. For example, If there exists heat flows from the south-east to the north-west, the kernel should give more weights to the nodes from the south-east when aggregating messages from neighbors. Using slight abuse of terminology, we consider the convolution kernels are equivalent to meteorological patterns in local regions.
+
+The signals are located on the earth surface, which is regarded as a sphere, and thus we introduce the notation of sphere manifold to further develop our convolution method. The $M$-D sphere manifold is denoted by $S^M = \{\bm{\mathrm{x}}^S = (x_1, x_2,\ldots, x_{M+1})\in \mathbb{R}^{M+1}:||\bm{\mathrm{x}}^S|| = 1\}$. The convolution is usually operated on a plane, so we introduce the **local space**, the $M$-D Euclidean space, as the convolution domains.
+
+::: Definition
+**Definition 1**. *Define the local space centered at point $\bm{\mathrm{x}}$ as some Euclidean space denoted by $\mathcal{L}_{\bm{\mathrm{x}}}S^M$, with $\bm{\mathrm{x}} \in \mathcal{L}_{\bm{\mathrm{x}}}S^M$, which is homeomorphic to the local region centered at $\bm{\mathrm{x}}$. (Formal definition see Appendix A2.)*
+:::
+
+::: Example
+**Example 2**. *The tangent space centered at point $\bm{\mathrm{x}}$ is an example of local space, denoted by $\mathcal{T}_{\bm{\mathrm{x}}}S^M = \{\bm{\mathrm{v}}\in \mathbb{R}^{M+1}: <\bm{\mathrm{x}}, \bm{\mathrm{v}}> = 0\}$, where $<\cdot,\cdot>$ is the Euclidean inner product.*
+:::
+
+The **geodesics and induced distance** on sphere are important to both defining the neighborhood of a node, as well as identifying the message-passing patterns. Intuitively, the greater is the distance from one node to another, the fewer messages should be aggregated from the node into another in graph convolution.
+
+::: Proposition
+**Proposition 1**. *Let $\bm{\mathrm{x}} \in S^{M}$, and $\bm{\mathrm{u}} \in \mathcal{T}_{\bm{\mathrm{x}}}S^M$ be unit-speed. The unit-speed geodesics is $\gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(t) = \bm{\mathrm{x}} \cos t + \bm{\mathrm{u}} \sin t$, with $\gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(0) = \bm{\mathrm{x}}$ and $\dot \gamma_{\bm{\mathrm{x}}\rightarrow\bm{\mathrm{u}}}(0) = \bm{\mathrm{u}}$. The intrinsic shortest distance function between two points $\bm{\mathrm{x}}, \bm{\mathrm{y}} \in S^M$ is $$\begin{align}
+ d_{S^M}(\bm{\mathrm{x}}, \bm{\mathrm{y}}) = \arccos(<\bm{\mathrm{x}},\bm{\mathrm{y}}>).
+\end{align}$$*
+:::
+
+The distance function is usually called great-circle distance on sphere. In practice, the K-nearest neighbors algorithm to construct the graph structure is conducted based on spherical distance.
+
+On the establishment of local space of each center node, an **isometric map** $\mathcal{M}_{\bm{\mathrm{x}}}(\cdot): S^M \rightarrow \mathcal{L}_{\bm{\mathrm{x}}}S^M$ satisfying that $||\mathcal{M}_{\bm{\mathrm{x}}}(\bm{\mathrm{y}})|| = d_{S^M}(\bm{\mathrm{x}},\bm{\mathrm{y}})$ can be used to map neighbor nodes on sphere into the local space.
+
+::: {#ex:logmap .Example}
+**Example 3**. *Logarithmic map is usually used to map the neighbor node $\bm{\mathrm{x}}_j\in \mathcal{V}(i)$ on sphere isometrically into $\mathcal{T}_{\bm{\mathrm{x}}_i}S^M$, which reads $$\begin{align*}
+ \log_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j) &= d_{S^M}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j)\frac{P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}_j - \bm{\mathrm{x}}_i)}{||P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}_j - \bm{\mathrm{x}}_i)||} ,
+\end{align*}$$ where $P_{{\bm{\mathrm{x}}_i}}(\bm{\mathrm{x}}) = \frac{\bm{\mathrm{x}}}{||\bm{\mathrm{x}}||} - <\frac{\bm{\mathrm{x}}_i}{||\bm{\mathrm{x}}_i||},\frac{\bm{\mathrm{x}}}{||\bm{\mathrm{x}}||}>\frac{\bm{\mathrm{x}}_i}{||\bm{\mathrm{x}}_i||}$ is the normalized projection operator.*
+:::
+
+After the neighbors of $\bm{\mathrm{x}}_i$ are mapped into local space of the center nodes through the isometric maps, which reads $\bm{\mathrm{v}}_j = \mathcal{M}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j)$, the **local coordinate system** of each center node is set up, through a transform mapping $\Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}_j) = \bm{\mathrm{x}}_j^{i'}$ for each $\bm{\mathrm{x}}_j \in \mathcal{V}(i)$. We call $\bm{\mathrm{x}}_j^{i'}$ the relative position of $\bm{\mathrm{v}}_j$ in the local coordinate system of the local space centered at $\bm{\mathrm{x}}_i$. As $\bm{\mathrm{x}}_j^{i'}$ is always in the local space which is Euclidean, the supper-script $E$ is omitted. The mapping $\Pi_{\bm{\mathrm{x}}_i}(\cdot)$ can be determined by $M$ orthogonal basis chosen in the local coordinate system, i.e. $\{\bm{\xi}^1, \bm{\xi}^2, \ldots, \bm{\xi}^M\}$, which will be discussed later in $S^2$ scenario for meteorological application.
+
+Given a center node ${\bm{\mathrm{x}}_i^E} \in \mathbb{R}^2$, from the perspective of defined graph convolution in Eq. [\[eq:graphconv\]](#eq:graphconv){reference-type="ref" reference="eq:graphconv"}, the convolution on planar mesh grids such as pixels on images is written as $$\begin{align}
+ (\Omega \star_{\mathcal{V}{(i)}} \bm{H})(\bm{\mathrm{x}}_i^E) &= \sum_{\bm{\mathrm{x}}^E} \Omega(\bm{\mathrm{x}}^E; \bm{\mathrm{x}}^E_i) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E)\notag\\
+ &=\sum_{\bm{\mathrm{x}}^E} \chi(\bm{\mathrm{x}}^E_i - \bm{\mathrm{x}}^E) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E),
+\end{align}$$ where $\delta_{\mathcal{A}}(\bm{\mathrm{x}}) = 1$ if $\bm{\mathrm{x}} \in \mathcal{A}$, else $0$. In terms of convolution on 2-D images, $\mathcal{V}(i) = \{\bm{\mathrm{x}}^E :\bm{\mathrm{x}}^E - \bm{\mathrm{x}}_i^E \in \mathbb{Z}^2\cap ([-k_1, k_1] \times [-k_2, k_2])\}$. $k_1 > 0$ and $k_2 >0$ are the convolution views to restrict how far away pixels are included in the neighborhood along the width-axis and length-axis respectively. When $k_1, k_2 < +\infty$, the neighborhood set is limited, and thus the convolution is defined as **local**, conducted on each node's local space, with **local convolution kernel** $\chi(\cdot)$ .
+
+To extend the local convolution on generalized manifolds, we conclude that the local space of $\bm{\mathrm{x}}^E_i$ is $\mathcal{L}_{\bm{\mathrm{x}}_i^E}\mathbb{R}^2 = \{\bm{\mathrm{x}}^E :\bm{\mathrm{x}}^E - \bm{\mathrm{x}}_i^E \in[-k_1, k_1] \times [-k_2, k_2]\}$, so that the isometric map satisfies $\bm{\mathrm{v}}^E = \mathcal{M}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}^E) = \bm{\mathrm{x}}^E - \bm{\mathrm{x}}^E_i$. $\{-\bm{e}_{x}, -\bm{e}_y\}$ with $\bm{e}_{x}=(1,0)$ and $\bm{e}_{y}=(0,1)$ is the orthogonal basis in local coordinate system of the local space. In conclusion, $$\begin{align}
+ \bm{\mathrm{x}}^{i'}=\Pi_{\bm{\mathrm{x}}_i^E}(\bm{\mathrm{v}}^E) = -\bm{\mathrm{v}}^E = \bm{\mathrm{x}}_i^E - \bm{\mathrm{x}}^E .
+\end{align}$$ In this way, we obtain the local convolution on 2-D Euclidean plane, which reads $$\begin{align}
+ (\Omega \star_{\mathcal{V}{(i)}} \bm{H})(\bm{\mathrm{x}}_i^E) &= \sum_{\bm{\mathrm{x}}^E} \chi(\bm{\mathrm{x}}^{i'}) \bm{H}(\bm{\mathrm{x}}^E)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^E).
+\end{align}$$ In analogy to this, the local convolution on 2-D spherical the center node $\bm{\mathrm{x}}^S_i$ is defined similarly: $$\begin{align}
+ (\Omega \star_{\mathcal{V}{(i)}} \bm{H}) (\bm{\mathrm{x}}_i^E)
+ &=\sum_{\bm{\mathrm{x}}^S} \chi(\bm{\mathrm{x}}^{i'}) \bm{H}(\bm{\mathrm{x}}^S)\delta_{\mathcal{V}(i)}(\bm{\mathrm{x}}^S),
+\end{align}$$ where $\mathcal{V}(i)$ is given by the graph structure, nodes in which can be mapped into $\bm{\mathrm{x}}_i^S$'s local space. And $\bm{\mathrm{x}}^{i'}$ is obtained by: $$\begin{align}
+ \bm{\mathrm{x}}^{i'}=\Pi_{\bm{\mathrm{x}}_i^S}(\bm{\mathrm{v}}^S) = \Pi_{\bm{\mathrm{x}}_i^S}(\mathcal{M}_{\bm{\mathrm{x}}_i^S}(\bm{\mathrm{x}}^S)) .
+\end{align}$$ Following parts are organized to discuss how to elaborate
+
+- $\mathcal{M}_{\bm{\mathrm{x}}_i^S}(\cdot)$ and $\Pi_{\bm{\mathrm{x}}_i^S}(\cdot)$, isometry mapping neighbors into some local space of ${\bm{\mathrm{x}}_i^S}$ and choice of orthogonal basis in local coordinate system.
+
+- $\chi(\cdot)$, the formulation of convolution kernel to approximate and imitate the meteorological patterns.
+
+In the following parts, all the nodes are located on sphere, so the supper-script $S$ is omitted. We choose what we define as **cylindrical-tangent space** and **horizon maps** (Fig. 1(a).) to construct local spaces and to map neighbors into them.
+
+::: Definition
+**Definition 2**. *For $\bm{\mathrm{x}}_i \in S^2$, the cylindrical-tangent space centered at $\bm{\mathrm{x}}_i$ reads $$\begin{align*}
+ \mathcal{C}_{\bm{\mathrm{x}}_i} S^2 = \{\bm{\mathrm{v}}\in \mathbb{R}^3:<\bm{\mathrm{v}}^-, \bm{\mathrm{x}}_i^-> = 0\},
+\end{align*}$$ where $\bm{\mathrm{x}}^- = (x_1, x_2)$, taking the first two coordinates of vectors in $\mathbb{R}^3$.*
+:::
+
+::: Proposition
+**Proposition 2**. *Similar to logarithmic map, the horizon map $\mathcal{H}_{\bm{\mathrm{x}}_i}(\cdot)$ is used to map the neighbor node $\bm{\mathrm{x}}_j\in \mathcal{V}(i)$ isometrically into $\mathcal{C}_{\bm{\mathrm{x}}_i} S^2$, which reads $$\begin{align*}
+ \mathcal{H}_{\bm{\mathrm{x}}_i}(\bm{\mathrm{x}}_j) = d_{S^2}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j)\frac{[P_{{\bm{\mathrm{x}}_i^-}}(\bm{\mathrm{x}}_j^- - \bm{\mathrm{x}}_i^-), x_{j,3} - x_{i,3}]}{||[P_{{\bm{\mathrm{x}}_i^-}}(\bm{\mathrm{x}}_j^- - \bm{\mathrm{x}}_i^-), x_{j,3} - x_{i,3}]||},
+\end{align*}$$ where $[\cdot,\cdot]$ is the concatenation of vectors/scalars.*
+:::
+
+
+
+(a) shows tangent space with logarithmic maps in the top and cylindrical-tangent space with horizon maps in the below. (b) shows the necessity of the unified standard for choice of the basis. xp from the east affects both xi and xj a lot, with the corresponding local patterns in two center nodes shown in the heatmaps. However, if the basis is not unified as given in the example, the smoothness of local convolution kernel will be compromised. (c) shows the motivation of reweighting the angle scale. The angle scale $\frac{\psi}{2\pi}$ is given to balance neighbors’ uneven contributions to the centers resulted from irregular distribution.
+
+
+The reason for choosing cylindrical-tangent space with horizon maps rather than tangent space with logarithmic maps is that the former one preserves the relative orientation on geographic graticules on the earth surface, which has explicitly geophysical meaning in meteorology. The logarithmic maps distort the relative position in orientation on graticules. For a node in the northern hemisphere, a neighbor locate in the east of it will locate in the north-east on its tangent plane after logarithmic map. In comparison, the defined cylindrical-tangent space preserves both relative orientation on graticules and spherical distance after mapping. Detailed proofs are provided Appendix B1 and empirical comparisons are given in Experiments 5.4.
+
+As discussed, the cylindrical-tangent space is Euclidean, so in $S^2$, the transform $\Pi_{\bm{\mathrm{x}}_i}(\cdot)$ can be determined by two orthogonal bases which are not unique. Since our method is mainly implemented in spherical meteorological signals, we choose $\{\bm{e}_{\phi},\bm{e}_z\}$ as the two orthogonal bases in every local coordinate system of the cylindrical-tangent plane, in order to permit every local space to share the consistent South and North poles and preserve the relative position. For $\bm{\mathrm{x}}_i = (x_{i,1}, x_{i,2}, x_{i,3})$ and $\bm{\mathrm{v}} \in \mathcal{C}_{\bm{\mathrm{x}}_i}S^2$, let $\phi_i = \arctan{(x_{i,2}/x_{i,1})}$, and $\bm{\mathrm{x}}^{i'} = \Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}) = (\theta^{i'}, z^{i'})$ which is obtained by $$\begin{align}
+ \phi^{i'} = <\bm{\mathrm{v}}, \bm{e}_{\phi_i}>;\quad
+ z^{i'} = <\bm{\mathrm{v}}, \bm{e}_{z_i}>,
+\end{align}$$ which is the latitude and longitude on the sphere, and $$\begin{align}
+ \label{eq:orthobasis}
+\bm{e}_{\phi_i} = (-\sin\phi_i, \cos\phi_i, 0); \quad
+\bm{e}_{z_i} = (0, 0, 1).
+\end{align}$$ The discussed maps and transforms cannot be applied for the South and North pole. We discuss it in Appendix B2.
+
+Now we introduce the conditional local convolution, which is the core module in our model. We aim to formulate a kernel which is
+
+- location-characterized: In the local regions of different center nodes, the meteorological patterns governed by convolution kernel differ.
+
+- smooth: Patterns are broadly similar when the center nodes are close in spatial distance.
+
+- common: The kernel is shared by different local spaces where the neighbors' spatial distribution is distinct.
+
+Contrary to Example 1 in DCRNN whose convolution kernel is predefined, we aim to propose the convolution kernel which can adaptively learn and imitate the location-characterized patterns of each local region centered at node $i$. A trivial way is to use a multi-layer neural network whose input is $\bm{\mathrm{x}}^{i'}$ to approximate the convolution kernel $\chi({\bm{\mathrm{x}}^{i'}})$. However, ${\bm{\mathrm{x}}^{i'}}$ as the input only represents the relative position and disables the kernel to capture the location-characterized patterns. For example, given two different center nodes whose neighbors' relative positions are totally the same, the convolution kernel in different locations will also coincide exactly, contrary to location-characterized patterns. Therefore, we propose to use conditional kernel, which reads $\chi({\bm{\mathrm{x}}^{i'}} ; \bm{\mathrm{x}}_{i})$, meaning that the convolution kernel in a certain local region is determined by the center node $\bm{\mathrm{x}}_{i}$. An multi-layer feedforward network is used to approximate this term, as $$\begin{align}
+ \chi({\bm{\mathrm{x}}^{i'}} ; \bm{\mathrm{x}}_{i}) = \mathrm{MLP}([\bm{\mathrm{x}}^{i'} , \bm{\mathrm{x}}_{i}]). \label{eq:mlpkernel}
+\end{align}$$
+
+We assume that the localized patterns of meteorological message flows have the property of **smoothness** -- two close center nodes' patterns of aggregation of messages from neighbors should be similar. In the light of convolution kernel, we define the smoothness of kernel function as follows:
+
+::: Definition
+**Definition 3**. *The conditional kernel $\chi(\cdot|\cdot)$ is smooth, if it satisfies that for any $\epsilon > 0$, there exist $\delta>0$, such that for any two points $\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j \in {S^2}$ with $d_{S^2}(\bm{\mathrm{x}}_i, \bm{\mathrm{x}}_j) \leq \delta$, $$\begin{align*}
+\sup_{\bm{\mathrm{v}}\in \mathcal{C}_{\bm{\mathrm{x}}_i} {S^2},\bm{\mathrm{u}}\in \mathcal{C}_{\bm{\mathrm{x}}_j} {S^2}\atop \Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}}) = \Pi_{\bm{\mathrm{x}}_j}(\bm{\mathrm{u}})} |\chi(\Pi_{\bm{\mathrm{x}}_i}(\bm{\mathrm{v}});\bm{\mathrm{x}}_j) - \chi(\Pi_{\bm{\mathrm{x}}_j}(\bm{\mathrm{u}});\bm{\mathrm{x}}_i)|\leq \epsilon.
+\end{align*}$$*
+:::
+
+The definition of smoothness of location-characterized kernel is motivated by the fact that if the distance between two center nodes $d_{S^2}(\bm{\mathrm{x}}_{i}, \bm{\mathrm{x}}_{j})$ is very small, the meteorological patterns in two local region should be of little difference, and thus kernel function $\chi(\cdot;\bm{\mathrm{x}}_{i})$ and $\chi(\cdot;\bm{\mathrm{x}}_{j})$ should be almost exactly the same.
+
+The unified standard for choice of orthogonal basis on the cylindrical-tangent plane avoids problems caused by path-dependent parallel transport [@cohen2019gauge], and contributes to the smoothness of conditional kernel. The property is likely to be compromised without unified standard for orthogonal basis, as the following example illustrates.
+
+::: Example
+**Example 4**. *(shown in Fig. [\[fig:exampleunify\]](#fig:exampleunify){reference-type="ref" reference="fig:exampleunify"}.) For one node $\bm{\mathrm{x}}_{i}$, the orthogonal basis is $\{\mathbf{e}_{\phi_i}, \mathbf{e}_{z_i}\}$ as previously defined in Eq. [\[eq:orthobasis\]](#eq:orthobasis){reference-type="ref" reference="eq:orthobasis"}, and for another node $\bm{\mathrm{x}}_{j}$ which is close to it, it is $\{-\mathbf{e}_{\phi_i}, \mathbf{e}_{z_i}\}$. There exists a node $\bm{\mathrm{x}}_{p}$ which is in their east on the sphere as the neighbors of both, and has great meteorological impacts on both of them. In one's local coordinate system, the first coordinate is positive while it is negative in another. Then if the kernel is smooth, the neighbor $\bm{\mathrm{x}}_{p}$ from the east will never be given large value in both local regions centered at node $i$ and $j$, violating the true patterns in meteorology, or it is likely to violate the smoothness of the kernel.*
+:::
+
+As such, by using $\mathrm{MLP}(\cdot)$ as the approximator with smooth activate function like $\tanh$ and unifying the standard for choice of orthogonal basis, the smoothness property of conditional kernel can be ensured. However, the irregular spatial distribution of discrete nodes conflicts with the continuous kernel function shared by different center nodes, which will be discussed in the next part.
+
+Because the kernel function is continuous and shared by different center nodes, when the spatial distribution of each node's neighbors is similar or even identical, e.g. nodes are distributed on regular spatial grids in local spaces, the proposed conditional kernel takes both distance and orientation into consideration. However, the nodes are discrete and irregularly distributed on the sphere. Since the kernel is shared by all center nodes, the distinct spatial distribution of neighbors of different center nodes is likely to disrupt the smoothness of local patterns. An explicit example is given to illustrate the problems brought about by it.
+
+::: Example
+**Example 5**. *(shown in Fig. [\[fig:examplereweight\]](#fig:examplereweight){reference-type="ref" reference="fig:examplereweight"}.) The two center nodes are close in distance, but the spatial distribution of their neighbors is different. The number of the right center's neighbors located in the south-west is two, while it is one for the left center. If the kernel is smooth, the message from the south-west flowing into the right center will be about twice than it from the south-west flowing into the left.*
+:::
+
+To reweight the convolution kernel for each $\bm{\mathrm{x}}_{j} \in \mathcal{V}(i)$, we consider both their angle and distance scales. We first turn its representation in Cartesian coordinate system $\bm{\mathrm{x}}_j^{i'} = (\phi^{i'}_j, z^{i'}_j)$ in cylindrical-tangent space of $\bm{\mathrm{x}}_i$ into polar coordinate $(\varphi^{i'}_j, \rho^{i'}_j)$, where $\varphi^{i'}_j = \arctan (z^{i'}_j/\phi^{i'}_j)$ and $\rho^{i'}_j = \sqrt{(z^{i'}_j)^2 + (\phi^{i'}_j)^2}$. Note that $\rho^{i'}_j$ equals to the geodesics induced distance between the two nodes on sphere. In terms of angle, we calculate the **angle bisector** of every pair of adjacent nodes in the neighborhood according to $\varphi^{i'}_j$. We denote the angle between two adjacent angular bisectors of $\bm{\mathrm{x}}_j^{i'}$ by $\psi^{i'}_j$ (as shown in lower-right subfigure in Fig. [\[fig:examplereweight\]](#fig:examplereweight){reference-type="ref" reference="fig:examplereweight"} ), and thus the angle scale is written as $\psi^{i'}_j/2\pi$. The distance scale is obtained similarly as DCRNN in Example 1, which reads $\exp(- (\rho^{i'}_j)^2/\tau)$, where $\tau$ is a learnable parameter.
+
+To sum up, combining the two scaling terms with Eq. [\[eq:mlpkernel\]](#eq:mlpkernel){reference-type="ref" reference="eq:mlpkernel"}, the final formulation of the smooth conditional local kernel in the case of irregular spatial distribution reads $$\begin{align}
+ \chi({\bm{\mathrm{x}}^{i'}_j} ; \bm{\mathrm{x}}_{i}) = \frac{\psi^{i'}_j}{2\pi}\exp(- \frac{(\rho^{i'}_j)^2}{\tau})\mathrm{MLP}([\bm{\mathrm{x}}^{i'}_j , \bm{\mathrm{x}}_{i}]). \label{eq:clckernel}
+\end{align}$$
+
+
+
+
+Different geographic sample spaces and local patterns in meteorology and traffic.
+
+
+The proposed convolution is inapplicable to traffic forecasting. One reason is that the smoothness is not a reasonable property of local traffic flows' patterns, i.e. great difference may exist between traffic patterns of two close regions. An important transportation hub exit may exist in the middle of them, so the patterns are likely to differ a lot. Besides, because our convolution kernel is continuous in spatial domain, the continuity in orientation of the local convolution kernel is of no physical meaning in traffic irregular networks. In essence, the irregular structure of the road network restricts the flows of traffic to road direction, stopping vehicles from crossing the road boundary, so that the geographic sample space is restricted to the road networks, and traffic can only flow along roads. In comparison, the flows in meteorology like heat and wind can diffuse freely on the earth, without boundary, and the geographic sample space is the whole earth surface, enabling the local patterns to satisfy the continuity and smoothness.
+
+
+
+Overall workflows and architecture of CLCRN.
+
+
+The temporal dynamics is modeled as DCRNN does, which replaces fully-connected layers in cells of recurrent neural network with graph convolution layers. Using the kernel proposed in Eq. [\[eq:clckernel\]](#eq:clckernel){reference-type="ref" reference="eq:clckernel"}, we obtain the GRU cell constituting the conditional local convolution recurrent network (CLCRN), whose overall architecture is shown in Fig. [1](#fig:overallarchi){reference-type="ref" reference="fig:overallarchi"}.
+
+The overall neural network architecture for multi-step forecasting is implemented based on Sequence to Sequence framework [@Sutskever2014seq2seq], with the encoder fed with previously observed time series and decoder generating the predictions. By setting a target function of predictions and ground truth observations such as minimal mean square error, we can use backpropagation through time to update the parameters in the training step. More details are given in Appendix C.
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+# Introduction
+
+In many real-world NLP applications, the collected data follow a skewed distribution [\(Deng et al.,](#page-9-0) [2009;](#page-9-0) [Fernandez et al.](#page-9-1) ´ , [2013;](#page-9-1) [Yan et al.,](#page-10-0) [2017\)](#page-10-0), i.e., data from a few classes appear much more frequently than those of other classes. For example, tweets related to incidents such as shooting or fire are usually rarer compared to those about sports or entertainments. These data instances often represent objects of interest as their rareness may carry important and useful knowledge [\(He](#page-9-2) [and Garcia,](#page-9-2) [2009;](#page-9-2) [Sun et al.,](#page-10-1) [2007;](#page-10-1) [Chen and Shyu,](#page-9-3) [2011\)](#page-9-3). However, most learning algorithms tend to inefficiently utilize them due to their disadvantage in the population [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4). Hence, learning discriminative models with imbalanced class distribution is an important and challenging task to the machine learning community.
+
+Solutions proposed in previous literature can be generally divided into three categories [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4): (1) *Data-level methods* that employ undersampling or over-sampling technique to balance the class distributions [\(Barua et al.,](#page-9-5) [2014;](#page-9-5) [Smith et al.,](#page-9-6) [2014;](#page-9-6) [Sobhani et al.,](#page-9-7) [2014;](#page-9-7) [Zheng et al.,](#page-10-2) [2015\)](#page-10-2). (2) *Algorithm-level methods* that modify existing learners to alleviate their bias towards the majority classes. The most popular branch is the *costsensitive* algorithms, which assign a higher cost on misclassifying the minority class instances. [\(D´ıaz-](#page-9-8)[Vico et al.,](#page-9-8) [2018\)](#page-9-8). (3) *Ensemble-based methods* that combine advantages of data-level and algorithmlevel methods by merging data-level solutions with classifier ensembles, resulting in robust and efficient learners [\(Galar et al.,](#page-9-9) [2012;](#page-9-9) [Wang et al.,](#page-10-3) [2015\)](#page-10-3).
+
+Despite the success of these approaches on many applications, some of their drawbacks have been observed. Resampling-based methods need to either remove lots of samples from the majority class or introduce a large amount of synthetic samples to the minority class, which may respectively lose important information or significantly increase the adverse correlation among samples [\(Wu et al.,](#page-10-4) [2017\)](#page-10-4). It is difficult to set the actual cost value in costsensitive approaches and they are often not given by expert before hand [\(Krawczyk,](#page-9-4) [2016\)](#page-9-4). Also, how to guarantee and utilize the diversity of classification ensembles is still an open problem in ensemble-based methods [\(Wu et al.,](#page-10-4) [2017;](#page-10-4) [Huo](#page-9-10) [et al.,](#page-9-10) [2016\)](#page-9-10).
+
+In this paper, we propose a novel *set convolution* (SetConv) operation and a new training strategy named as *episodic training* to assist learning from imbalanced class distributions. The proposed solution naturally addresses the drawbacks of existing methods. Specifically, SetConv explicitly learns the weights of convolution kernels based on the intra-class and inter-class correlations, and uses the learned kernels to extract discriminative features from data of each class. It then compresses these features into a single class representative. These representatives are later applied for classification. Thus, SetConv helps the model to *ignore samplespecific noisy information*, and *focuses on the la-*
+
+
+
+Figure 1: Overview of the proposed approach. (a) The training procedure of SetConv. At each iteration, SetConv is fed with an episode to evaluate the classification loss for model update. Each episode consists of a support set and a query set. The support set is formed by a group of samples where the imbalance ratio is preserved. The query set contains only one sample from each class. (b) The post training step of SetConv, which is performed only once after the main training procedure. In this step, we extract a representative for each class from the training data and will later use them for inference. Here we only perform inference using the trained model and do not update it. (c) The inference procedure of SetConv. Each query data is compared with every class representative to determine its label.
+
+*tent concept not only common to different samples of the same class but also discriminative to other classes.* On the other hand, in episodic training, we assign equal weights to different classes and do not perform resampling on data. Moreover, at each iteration during training, the model is fed with an episode formed by a set of samples where the class imbalance ratio is preserved. It encourages the model learning to *extract discriminative features even when class distribution is highly unbalanced*.
+
+Building models with SetConv and episodic training has several additional benefits:
+
+- (1) *Data-Sensitive Convolution.* By utilizing SetConv, each input sample is associated with a set of weights that are estimated based on its relation to the minority class. This data-sensitive convolution helps the model to customize the feature extraction process for each input sample, which potentially improves the model performance.
+- (2) *Automatic Class Balancing.* At each iteration, no matter how many data of a class is fed into the model, SetConv always extracts the most discriminative information from them and compress
+
+it into a single distributed representation. Thus, the subsequent classifier, which takes these class representatives as input, always perceives a balanced class distribution.
+
+(3) *No dependence on unknown prior knowledge.* The only prior knowledge needed in episodic training is the class imbalance ratio, which can be easily obtained from data in real-world applications.
+
+# Method
+
+Our goal is to develop a classification model that works well when the class distribution is highly unbalanced. For simplicity, we first consider a binary classification problem and later extend it to the multi-class scenario. As shown in Fig. [1a,](#page-1-0) our model is composed of a SetConv layer and a classification layer. At each iteration during training, the model is fed with an *episode* sampled from the training data, which is composed of a support set and a query set. The support set preserves the imbalance ratio of training data, and the query set contains only one sample from each class. Once the SetConv layer receives an episode, it extracts features for every sample in the episode and produces a representative for each class in the support set. Then, each sample in the query set is compared with these class representatives in classification layer to determine its label and evaluate the classification loss for model update. We refer this training procedure as *episodic training*.
+
+We choose episodic training due to following reasons: (1) It encourages the SetConv layer learning to extract discriminative features even when the class distribution of the input data is highly unbalanced. (2) Since the episodes are randomly sampled from data with significantly different configuration of support and query sets (i.e., data forming these sets vary from iteration to iteration), it requires the SetConv layer to capture the underlying class concepts that are common among different episodes.
+
+After training, a post training step is performed only once to extract a representative for each class from the training data, which will later be used for inference (Fig. [1b\)](#page-1-1). It is conducted by randomly sampling a large subset of training data (referred as Spost) and feeding them to the SetConv layer. *Note that we only perform inference using the trained model and do not update it in this step.* We can conduct this operation because the SetConv layer has learned to capture the class concepts, which are insensitive to the episode configuration during training. We demonstrate it in experiments and the result is shown in Section [4.6.](#page-8-0)
+
+The inference procedure of the proposed ap-
+
+
+
+Figure 2: Relations between the input samples and a pre-selected minority class anchor are used by SetConv to estimate both intra-class correlations and inter-class correlations.
+
+proach is straightforward (Fig. 1c). For each query sample, we extract its feature via the SetConv layer and then compare it with those class representatives obtained in post training step. The class that is most similar to the query is assigned as the predicted label.
+
+In many real-world applications, the minority class instances often carry important and useful knowledge that need intensive attention by the machine learning models (He and Garcia, 2009; Sun et al., 2007; Chen and Shyu, 2011).
+
+Based on this prior knowledge, we choose to design the SetConv layer in a way such that the feature extraction process focuses on the minority class. We achieve it by estimating the weights of the SetConv layer based on the relation between the input samples and a pre-selected minority class anchor. This anchor can be freely determined by the user. In this paper, we adopt a simple option, i.e., average-pooling of the minority class samples. Specifically, for each input variable, we compute its mean value across all the minority class samples in the training data. It is executable because the minority-class samples are usually limited in realworld applications1. As shown in Figure 2, this weight estimation method assists the SetConv layer in capturing not only the intra-class correlation of the minority class, but also the inter-class correlation between the majority and minority classes.
+
+Suppose $\mathcal{E}_t = \{\mathcal{S}_t, \mathcal{Q}_t\}$ is the episode sent to the SetConv layer at iteration t, where $\mathcal{S}_t = (X_{maj} \in \mathcal{R}^{N_1 \times d}, X_{min} \in \mathcal{R}^{N_2 \times d})$ is the support set and $\mathcal{Q}_t = (q_{maj} \in \mathcal{R}^{1 \times d}, q_{min} \in \mathcal{R}^{1 \times d})$ is the query set. In general, $X_{maj}, X_{min}, q_{maj}$ and $q_{min}$ can be considered as a sample set of size $N_1, N_2, 1$ and 1 respectively. For simplicity, we abstract this sample set into $X \in \mathcal{R}^{N \times d}, N \in \{N_1, N_2, 1\}$ .
+
+Remind that the standard discrete convolution is:
+
+$$h[n] = (f \star g)[n] = \sum_{m=-M}^{m=M} f[m]g[n-m]$$
+ (1)
+
+Here, f and g denote the feature map and kernel weights respectively.
+
+Similarly, in our case, we define the set convolution (SetConv) operation as:
+
+
+$$h[Y] = \frac{1}{N} \sum_{i=1}^{N} X_i \cdot g(Y - X_i)$$
+$$= \frac{1}{N} \left( X \circ g(Y - X) \right)$$
+(2)
+
+where $Y \in \mathcal{R}^{1 \times d}$ , $g(Y - X) \in \mathcal{R}^{N \times d \times d_o}$ and $h[Y] \in \mathcal{R}^{1 \times d_o}$ denote the minority class anchor, kernel weights and the output embedding respectively. Here, $\circ$ is the tensor dot product operator, i.e., for every $i \in \{1, 2, \dots, d_o\}$ , we compute the dot product of X and g(Y - X)[:,:,i].
+
+Unfortunately, directly learning g(Y-X) is memory intensive and computationally expensive, especially for large-scale high-dimensional data. To overcome this issue, we introduce an efficient method to approximate these kernel weights. Instead of taking X as a set of d-dimensional samples, we stack these samples and consider it as a giant dummy sample $X' = Concat(X) \in \mathcal{R}^{1 \times Nd}$ . Then, Eq. 2 is rewritten as
+
+$$h[Y] = \frac{1}{N} \Big( X' \cdot g'(Y - X) \Big) \tag{3}$$
+
+where $g'(Y-X) \in \mathcal{R}^{Nd \times d_o}$ is the transformed kernel weights. To efficiently compute g'(Y-X), we propose to approximate it as the *Khatri-Rao* product2 (Rabanser et al., 2017) of two individual components, i.e.,
+
+$$g'(Y - X) = g_1(Y - X) \circledast g_2(W)$$
+
+$$= MLP(Y - X; \theta) \circledast SoftMax(W, 0)$$
+(4)
+
+<sup>1Otherwise, we may sample a subset from the minority class samples to compute the anchor.
+
+<sup>2https://en.wikipedia.org/wiki/Kronecker\_product
+
+
+
+Figure 3: The computation graph of the SetConv layer. Here Y is a minority class anchor. $W \in \mathcal{R}^{d \times d_o}$ is a weight matrix to learn that records the correlation between the input and output variables. Specifically, the $i_{th}$ column of $g_2(W)$ gives the weight distribution over input features for the $i_{th}$ output feature. It is indeed a feature-level attention matrix. In addition, we estimate another data-sensitive weight matrix $g_1(Y-X)$ from the input data. The final convolution weight tensor is simply the Khatri-Rao product of $g_1(Y - X)$ and $g_2(W)$ .
+
+where $W \in \mathcal{R}^{d \times d_o}$ is a weight matrix that represents the correlation between input and output variables. $g_2(W)$ takes softmax over the first dimension of W, and is indeed a feature-level attention matrix. The $i_{th}$ column of $g_2(W)$ provides the weight distribution over input features for the $i_{th}$ output feature. On the other hand, $g_1(Y-X)$ is a data-sensitive weight matrix estimated from input data via a MLP by considering their relation to the minority class anchor. Similar to data-level attention, $g_1(Y - X)$ helps the model customize the feature extraction process for input samples, which potentially improves the model performance. Figure 3 shows the detailed computation graph of the SetConv layer.
+
+**Discussion:** An important property of the Set-Conv layer is *permutation-invariant*, i.e., it is insensitive to the order of input samples. As long as the input samples are same, no matter in which order they are sent to the model, the SetConv layer always produces the same feature representation. Mathematically, let $\pi$ denote an arbitrary permutation matrix, we have $SetConv(\pi X) =$ SetConv(X). The detailed proof of this property is provided in the supplementary material.
+
+Suppose the feature representation obtained from the SetConv layer for $X_{maj}$ , $X_{min}$ , $q_{maj}$ and $q_{min}$ in the episode are denoted by $v_{maj}^s$ , $v_{min}^s$ , $v_{maj}^q$ and $v_{min}^q$ respectively. The probability of predicting $v_{maj}^q$ or $v_{min}^q$ as the majority class is given by
+
+$$P(c=0|x) = \frac{\exp(x \odot v_{maj}^s)}{\exp(x \odot v_{maj}^s) + \exp(x \odot v_{min}^s)}$$
+(5)
+
+where o represents the dot product operation and $x \in \{v_{maj}^q, v_{min}^q\}.$ Similarly, the probability of predicting $v_{maj}^q$ or
+
+ $v_{min}^q$ as the minority class is
+
+$$P(c=1|x) = \frac{\exp(x \odot v_{min}^s)}{\exp(x \odot v_{maj}^s) + \exp(x \odot v_{min}^s)}$$
+(6)
+
+where $x \in \{v_{maj}^q, v_{min}^q\}$ .
+
+We adopt the well-known cross-entropy loss for error estimation and use the Adam optimizer to update model.
+
+Extending SetConv for multi-class imbalance learning is straightforward. We translate the multi-class classification problem into multiple binary classification problems, i.e., we create a one-vs-all classifier for each of the N classes. Specifically, for a class c, we treat those instances with label y = cas positive and those with $y \neq c$ as negative. The anchor is hence computed based on the smaller one of the positive and negative classes. The prediction probability P(y = c|x) for a given instance x is computed in a similar way as Eq. 5,
+
+$$P(y=c|x) = \frac{\exp(x \odot v_{y=c}^s)}{\exp(x \odot v_{y\neq c}^s) + \exp(x \odot v_{y=c}^s)}$$
+(7)
+
+Therefore, the predicted label of the instance x is $\operatorname{argmax}_{c} P(y = c | x).$
+
+We evaluate SetConv on two typical tasks, including incident detection on social media and sentiment classification.
+
+Table 1: Class distribution in the IRT dataset.
+
+| | Two Classes | | Four Classes | | | |
+|----------------|-------------|------|--------------|------|----------|------|
+| | Yes | No | Crash | Fire | Shooting | No |
+| Boston (USA) | 604 | 2216 | 347 | 188 | 28 | 2257 |
+| Sydney (AUS) | 852 | 1991 | 587 | 189 | 39 | 2208 |
+| Brisbane (AUS) | 689 | 1898 | 497 | 164 | 12 | 1915 |
+| Chicago (USA) | 214 | 1270 | 129 | 81 | 4 | 1270 |
+| Dublin (IRE) | 199 | 2616 | 131 | 33 | 21 | 2630 |
+| London (UK) | 552 | 2444 | 283 | 95 | 29 | 2475 |
+| Memphis (USA) | 361 | 721 | 23 | 30 | 27 | 721 |
+| NYC (USA) | 413 | 1446 | 129 | 239 | 45 | 1446 |
+| SF (USA) | 304 | 1176 | 161 | 82 | 61 | 1176 |
+| Seattle (USA) | 800 | 1404 | 204 | 153 | 139 | 390 |
+
+Table 2: Class distribution in Amazon Review and SemiEval Datasets.
+
+| Dataset | Negative | Positive | IR |
+|--------------------|----------|----------|------|
+| Amazon-Books | 72039 | 7389 | 9.75 |
+| Amazon-Electronics | 13560 | 1908 | 7.11 |
+| Amazon-Movies | 12896 | 2066 | 6.24 |
+| SemiEval | 39123 | 7273 | 5.38 |
+
+We conduct experiments on a real-world benchmark *Incident-Related Tweet*3 (Schulz et al., 2017) (**IRT**) dataset. It contains 22, 170 tweets collected from 10 cities, and allows us to evaluate our approach against geographical variations. The IRT dataset supports two different problem settings: binary classification and multi-class classification. In binary classification, each tweet is either "incident-related" or "not incident-related". In multi-class classification, each tweet belongs to one of the four categories including "crash", "fire", "shooting" and a neutral class "not incident related". The details of this dataset are shown in Table 1.
+
+We conduct experiments on two large-scale benchmark datasets, including *Amazon Review*4 (He and McAuley, 2016) and *SemiEval*5 (Rosenthal et al., 2017), which have been widely used for sentiment classification. Similar to MSDA (Li et al., 2019) and SCL-MI (Blitzer et al., 2007), we treat the amazon reviews with rating > 3 as positive examples, those with rating < 3 as negative examples, and discard the rest because their polarities are ambiguous. In addition, due to the tremendous size of Amazon Review dataset, we choose its 3 largest categories, i.e., "Books", "Electronics", and "Movies and TV",
+
+and uniformly sample from these categories to form a subset that contains 109, 858 reviews. This subset is sufficiently large to evaluate the effectiveness of our method. More importantly, the imbalance ratio of each category in this subset is exactly same as that in the original dataset. Details of Amazon Review and SemiEval datasets are listed in Table 2.
+
+We compare our algorithm with several state-ofthe-art imbalance learning methods.
+
+- **IHT** (Smith et al., 2014) (*under-sampling*) is a model that performs undersampling based on instance hardness.
+- WEOB2 (Wang et al., 2015) (ensemble) is an undersampling based ensemble model that effectively adjusts the learning bias from the majority class to the minority class via adaptive weight adjustment. It only supports binary classification.
+- **KMeans-SMOTE** (Last et al., 2017) (*over-sampling*) is an oversampling technique that avoids the generation of noisy samples and effectively overcomes the imbalance between classes.
+- IML (Wang et al., 2018) (*metric learning*) is a method that utilizes metric learning to explore the correlations among imbalance data and constructs an effective data space for classification.
+- **CS-DMLP** (Díaz-Vico et al., 2018) (*cost-sensitive*) is a deep MLP model that utilizes cost-sensitive learning to regularize the posterior probability distribution predicted for a given sample.
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+# Introduction
+
+
+
+In a binary classification setup, the reaction of the reader is unclear. Here, we use Misinfo Reaction Frames to understand how the reader perceives and reacts to the headline. Our pragmatic frames explain how a health or climate news article is interpreted as reliable or misinformation by readers by incorporating not only linguistic knowledge (e.g. emotions invoked by certain content words), but also knowledge of common social behaviors and domain-specific reasoning. We also include fact-checked labels (gold label).
+
+
+:::: table*
+::: tabular
+F10emF11 emF11.5emcc **News Headline** & **Writer's Intent** & **Reader Reaction** & **Spread** & **Real News?**\
+& & & & (GPT-2 / T5 / Gold)\
+How COVID is Affecting U.S. Food Supply Chain & **Human**: "the pandemic is interrupting the flow of groceries to consumers\"\
+**GPT-2**: "food supplies are being affected by covid\"\
+**T5**: "food supply chain is affected by covid\" & **Human**: "want to know how their groceries will get to them\"\
+**GPT-2**: "want to learn more\"\
+**T5**:"want to find out more information\" & 4.0 & / /\
+\
+Thai police arrested a cat for disobeying the curfew order. & **Human**: "governments are ludicrous and obtuse.\"\
+**GPT-2**: "animals can be dangerous\"\
+**T5**: "lockdowns are enforced in thailand\" &
+
+**Human**: "feel disbelief\"\
+**GPT-2**: "feel worried\"\
+**T5**: "feel shocked\" & 1.0 & / /\
+\
+Perspective \| I'm a black climate expert. Racism derails our efforts to save the planet. & **Human**: "since climate change will likely affect poorer nations, rich societies are not motivated to help\"\
+**GPT-2**: "racism is bad\"\
+**T5**: "racism is a problem in society\" & **Human:** "want to improve their own behavior towards others\"\
+**GPT-2:** "want to learn more\"\
+**T5**: "want to take action\" & 3.0 & / /\
+:::
+::::
+
+> *Many objects, persons, and experiences in the world are framed in terms of their potential role in supporting, harming, or enhancing people's lives or interests. We can know that this is so if we know how to interpret expressions in which such things are evaluated\...*\
+> - Charles J. Fillmore (1976)
+
+Effectively predicting how a headline may influence a reader requires knowledge of how readers perceive the intent behind real and fake news. While most prior NLP research on misinformation has focused on fact-checking, preventing spread of misinformation goes beyond determining veracity [@schuster2020limitations; @social_motives].
+
+For example, in Figure [1](#fig:mars){reference-type="ref" reference="fig:mars"}, mistrust in the government may lead readers to share pandemic conspiracy headlines like *"Epidemics and cases of disease in the 21st century are "staged\"\"* even if they suspect it is misinformation. The widespread circulation of misinformation can have serious negative repercussions on readers --- it can reinforce sociopolitical divisions like anti-Asian hate [@Vidgen2020DetectingEA; @voterfraud], worsen public health risks [@10.1145/3274327], and undermine efforts to educate the public about global crises [@climate].
+
+We introduce **Misinfo Reaction Frames** (MRF), a pragmatic formalism to reason about the effect of news headlines on readers. Inspired by Frame semantics [@Fillmore1976FRAMESA], our frames distill the pragmatic implications of a news headline in a structured manner. We capture free-text explanations of readers reactions and perceived author intent, as well as categorical estimates of veracity and likelihood of spread (Table [\[table:dims\]](#table:dims){reference-type="ref" reference="table:dims"}). We use our new formalism to collect the MRF corpus, a dataset of 202.3k news headline/annotated dimension pairs (69.8k unique implications for 25.1k news headlines) from Covid-19, climate and cancer news.
+
+We train reaction inference models to predict MRF dimensions from headlines. As shown by Table [\[table:examples_data\]](#table:examples_data){reference-type="ref" reference="table:examples_data"}, reaction inference models can correctly label the veracity of headlines (85% F1) and infer commonsense knowledge like *"a cat being arrested for disobeying curfew $\implies$ lockdowns are enforced.\"* However, models struggle with more nuanced implications *"a cat arrested for disobeying curfew $\implies$ government incompetence.\"* We test generalization of reaction frame inference on a new cancer domain and achieve 86% F1 by finetuning our MRF model on 574 annotated examples.
+
+:::: table*
+::: tabular
+llF18emF12em **Dimension** & **Type** & **Description** & **Example**\
+Writer Intent & free-text & A writer intent implication captures **the readers' interpretation of what the writer is implying.** & "some masks are better than others.\"\
+&\
+Reader Perception & free-text & A reader perception implication describes how readers would *feel* in response to a headline. These inferences include **emotional reactions** and **observations**. & "feeling angry.\", "feeling that the event described in the headline would trouble most people.\"\
+&\
+Reader Action & free-text & A reader action implication captures what readers would *do* in response to a headline. These describe **actions**. & "buy a mask.\"\
+&\
+Likelihood of Spread & ordinal & To take into account variability in impact of misinformation due to low or high appeal to readers, we use a 1-5 Likert [@Likert1932ATF] scale to measure the **likelihood of an article being shared or read**. Categories are {*Very Likely*, *Likely*, *Neutral*, *Unlikely*, *Very Unlikely*}. & 4/5\
+&\
+Perceived Label & binary & We elicit the perceived label (real/misinfo) of a headline, i.e. **whether it appears to be misinformation or real news to readers**. & real\
+&\
+Gold Label & binary & We include the **original ground-truth headline label** (real/misinfo) that was verified by fact-checkers. & misinfo\
+:::
+::::
+
+To showcase the usefulness of the MRF framework in user-facing interventions, we investigate the effect of MRF explanations on reader trust in headlines. Notably, in a user study our results show that machine-generated MRF inferences affect readers' trust in headlines and for the best model there is a statistically significant correlation (Pearson's $r$=0.24, $p$=0.018) with labels of trustworthiness (§[5.3](#sec:gen){reference-type="ref" reference="sec:gen"}).
+
+Our framework and corpus highlight the need for reasoning about the pragmatic *implications* of news headlines with respect to reader reactions to help combat the spread of misinformation. We publicly release the MRF corpus and trained models to enable further work ().[^1] We explore promising future directions (and limitations) in (§[6](#sec:future_work){reference-type="ref" reference="sec:future_work"}).
+
+In contrast to prior work on misinformation detection [@ott-etal-2011-finding; @rubin-etal-2016-fake; @rashkin-etal-2017-truth; @Wang2017LiarLP; @Hou2019TowardsAD; @volkova-etal-2017-separating; @10.1145/3274351] which mostly focuses on linguistic or social media-derived features, we focus on the potential impact of a news headline by modeling readers' reactions. This approach is to better understand how misinformation can be countered, as it has been shown that interventions from AI agents are better at influencing readers than strangers [@Kulkarni2013AllTN].
+
+In order to model impact, we build upon prior work that aims to describe the rich interactions involved in human communication, including semantic frames [@Fillmore1976FRAMESA], the encoder-decoder theory of media [@Hall1973EncodingAD][^2], Grice's conversational maxims [@grice1975logic] and the rational speech act model [@GOODMAN2016818][^3]. By describing these interactions with free-text implications invoked by a news headline, we also follow from prior work on pragmatic frames of connotation and social biases [@speer-havasi-2012-representing; @rashkin-etal-2018-event2mind; @Sap2019ATOMICAA; @socialbf; @socialc].
+
+While approaches like rational speech acts model both a pragmatic speaker and listener, we take a **reader-centric** approach to interpreting "intent\" of a news headline given that the writer's intent is challenging to recover in the dynamic environment of social media news sharing [@10.1145/3359229]. By bridging communication theory, data annotation schema and predictive modeling, we define a concrete framework for understanding the impact of a news headline on a reader.
+
+Table [\[table:examples_data\]](#table:examples_data){reference-type="ref" reference="table:examples_data"} shows real and misinformation news examples from our dataset with headlines obtained from sources described in §[3.1](#sec:data){reference-type="ref" reference="sec:data"}. We pair these headline examples with generated reaction frame annotations from the MRF corpus. Each reaction frame contains the dimensions in Table [\[table:dims\]](#table:dims){reference-type="ref" reference="table:dims"}.
+
+We elicit annotations based on a *news headline*, which summarizes the main message of an article. We explain this further in §[3.1](#sec:data){reference-type="ref" reference="sec:data"}. An example headline is "*Covid-19 may strike more cats than believed*.\" To simplify the task for annotators and ground implications in real-world concerns, we define these implications as relating to one of 7 common themes (e.g. technology or government entities) appearing in Covid and climate news.[^4] We list all the themes in Table [1](#table:themes){reference-type="ref" reference="table:themes"}, with some themes being shared between topics.
+
+To construct a corpus for studying reader reactions to news headlines, we obtain 69,885 news implications (See §[3.1](#sec:data){reference-type="ref" reference="sec:data"}) by eliciting annotations for 25,164 news headlines (11,757 Covid related articles, 12,733 climate headlines and 674 cancer headlines). There are two stages for collecting the corpus - (1) news data collection and (2) crowd-sourced annotation.
+
+A number of definitions have been proposed for labeling news articles based on reliability. To scope our task, we focus on false news that may be unintentionally spread (misinformation). This differs from disinformation, which assumes a malicious intent or desire to manipulate [@Fallis2014AF]. We examine reliable and unreliable headline extracted from two domains with widespread misinformation: Covid-19 [@hossain-etal-2020-covidlies] and climate change [@lett8fake]. We additionally test on cancer news [@10.1145/3394486.3403092] to measure out-of-domain performance.
+
+We retrieve both trustworthy and misinformation headlines related to climate change from NELA-GT-2018-2020 [@gruppi2020nelagt2019; @norregaard2019nelagt2018], a dataset of news articles from 519 sources. Each source in this dataset is labeled with a 3-way trustworthy score (reliable / sometimes reliable / unreliable). We discard articles from "sometimes reliable\" sources since the most appropriate label under a binary labeling scheme is unclear. To identify headlines related to climate change, we use keyword filtering.[^5] We also use claims from the SciDCC dataset [@mishraneuralnere], which consists of 11k real news articles from ScienceDaily,[^6] and Climate-FEVER [@diggelmann2021climatefever], which consists of more than 1,500 true and false climate claims from Wikipedia. We extract claims with either supported or refuted labels in the original dataset.[^7]
+
+::: {#table:themes}
+ Theme Climate Covid
+ ---------------------- --------- -------
+ Climate Statistics
+ Natural Disasters
+ Entertainment
+ Ideology
+ Disease Transmission
+ Disease Statistics
+ Health Treatments
+ Protective Gear
+ Government Entities
+ Society
+ Technology
+
+ : Themes present in articles by each news topic. Some are covered by both climate and Covid domains, while others are domain specific.
+:::
+
+::: {#table:both_stats}
+ Statistic Train Dev. Test Cancer
+ ----------------- --------- -------- -------- --------
+ Headlines 19,897 2,460 2,133 674
+ Unique Intents 38,172 4,867 4,388 1,232
+ Unique Percept. 2,609 538 421 174
+ Unique Actions 15,036 2,176 1,739 704
+ Total Pairs 159,564 19,700 17,890 5,227
+
+ : Dataset-level breakdown of statistics for MRF corpus.
+:::
+
+[]{#table:both_stats label="table:both_stats"}
+
+For trustworthy news regarding Covid-19, we use the CoAID dataset [@cui2020coaid] and a Covid-19 related subset of NELA-GT-2020 [@gruppi2020nelagt2019]. CoAID contains 3,565 news headlines from reliable sources. These headlines contain Covid-19 specific keywords and are scraped from nine trustworthy outlets (e.g. the World Health Organization).
+
+For unreliable news (misinformation), we use The CoronaVirusFacts/DatosCoronaVirus Alliance Database, a dataset of over 10,000 mostly false claims related to Covid-19 and the ESOC Covid-19 Misinformation Dataset, which consists of over 200 additional URLs for (mis/dis)information examples.[^8][^9] These claims originate from social media posts, manipulated media, and news articles, that have been manually reviewed and summarized by fact-checkers.
+
+We construct an evaluation set for testing out-of-domain performance using cancer real and misinformation headlines from the DETERRENT dataset [@10.1145/3394486.3403092], consisting of 4.6k real news and 1.4k fake news articles.
+
+In this section we outline the structured annotation interface used to collect the dataset. Statistics for the full dataset are provided in Table [2](#table:both_stats){reference-type="ref" reference="table:both_stats"}.
+
+We use the Amazon Mechanical Turk (MTurk) crowdsourcing platform.[^10] We provide Figure [\[fig:marsannotation2\]](#fig:marsannotation2){reference-type="ref" reference="fig:marsannotation2"} in the Appendix to show the layout of our annotation task. For ease of readability during annotation, we present a headline summarizing the article to annotators, rather than the full text of the article. Annotators then rate veracity and likelihood of spread based on the headline, as well as providing free-text responses for writer intent, reader perception and reader action.[^11] We structure the annotation framework around the themes described in §[2](#sec:dims){reference-type="ref" reference="sec:dims"}.
+
+We use a three-stage annotation process for ensuring quality control. In the initial pilot, we select a pool of pre-qualified workers by restricting to workers located in the US who have had at least 99% of their *human intelligence tasks* (hits) approved and have had at least 5000 hits approved. We approved workers who consistently submitted high-quality annotations for the second stage of our data annotation, in which we assessed the ability of workers to discern between misinformation and real news. We removed workers whose accuracy at predicting the label (real/misinfo) of news headlines fell below 70%. Our final pool consists of 80 workers who submitted at least three annotations during the pilot tasks. We achieve pairwise agreement of 79% on the label predicted by annotators during stage 3, which is comparable to prior work on Covid misinformation [@hossain-etal-2020-covidlies]. To account for chance agreement, we also measure Cohen's Kappa $\kappa = .51$, which is considered "moderate" agreement. Additional quality control measures were taken as part of our extensive annotation post-processing. For details, see Appendix [\[sec:postp\]](#sec:postp){reference-type="ref" reference="sec:postp"}.
+
+We provided an optional demographic survey to MTurk workers during annotation. Of the 63 annotators who reported ethnicity, 82.54% identified as White, 9.52% as Black/African-American, 6.35% as Asian/Pacific Islander, and 1.59% as Hispanic/Latino. For self-identified gender, 59% were male and 41% were female. Annotators were generally well-educated, with 74% reporting having a professional degree, college-level degree or higher. Most annotators were between the ages of 25 and 54 (88%). We also asked annotators for their preferred news sources. New York Times, CNN, Twitter, Washington Post, NPR, Reddit, Reuters, BBC, YouTube and Facebook were reported as the 10 most common news sources.
+
+We test the ability of large-scale language models to predict Misinfo Reaction Frames. For free-text inferences (e.g. writer intent, reader perception), we use generative language models, specifically T5 encoder-decoder [@Raffel2020ExploringTL] and GPT-2 decoder-only models [@radford2019language]. For categorical inferences (e.g. the gold label), we use either generative models or BERT-based discriminative models [@devlin-etal-2019-bert]. We compare neural models to a simple retrieval baseline (**BERT-NN**) where we use gold implications aligned with the most similar headline from the training set.[^12]
+
+For generative models, we use the following input sequence $$x = h_1~...~h_T || s_{d} || s_{t},$$ where $h$ is a headline of length $T$ tokens, $s_t \in \{$\[*covid*\]$,$\[*climate*\]$\}$ is a special topic control token, and $s_d$ is a special dimension control token representing one of six reaction frame dimensions. Here $||$ represents concatenation. The output is a short sequence representing the predicted inference (e.g. "*to protest*" for reader action, "*misinfo*" for the gold label). For GPT-2 models we also append the gold output inference $y = g_1~...~g_N$ during training, where $N$ is the length of the inference.
+
+We predict each token of the output inference starting from the topic token $s_t$ until the \[$eos$\] special token is generated. In the case of data with unknown topic labels, this allows us to jointly predict the topic label and output inference. We decode using beam search, since generations by beam search are known to be less diverse but more factually aligned with the context [@massarelli-etal-2020-decoding].
+
+For discriminative models, we use the following input sequence $$x = [CLS] h_1~...~h_T [SEP],$$ where \[CLS\] and \[SEP\] are model-specific special tokens. The output is a categorical inference.
+
+All our models are optimized using cross-entropy loss, where generally for a sequence of tokens $t$ $$CE(t) = - \frac{1}{|t|}\sum_{i=1}^{|t|} log P_\theta(t_i|t_1,...,t_{i-1}).$$ Here $P_\theta$ is the probability given a particular language model $\theta$. Since GPT-2 does not explicitly distinguish between the input and output (target) sequence during training, we take the loss with respect to the full sequence. For T5 we take the loss with respect only to the output.
+
+To improve generalization of MRF models, we use an additional masked fine-tuning step. We first train a language model $\theta$ on a set of Covid-19 training examples $D_{covid}$ and climate training examples $D_{climate}$. Then we use the Textrank algorithm [@mihalcea-tarau-2004-textrank] to find salient keyphrases in $D_{covid}$ and $D_{climate}$, which we term $k_{covid}$ and $k_{climate}$ respectively. We determine domain-specific keyphrases by looking at the complement of $k_{covid} \cap k_{climate}$ $$\begin{multline*}
+ k'_{covid} = k_{covid} \setminus k_{covid} \cap k_{climate} \\
+ k'_{climate} = k_{climate} \setminus k_{covid} \cap k_{climate},
+\end{multline*}$$ and only keep the top 100 keyphrases for each domain. We mask out these keyphrases in the training examples from $D_{covid}$ and $D_{climate}$ by replacing them with a $$ token. Then we continue training by fine-tuning on the masked examples. A similar approach has been shown to improve generalization and reduce shortcutting of reasoning in models for event detection [@Liu2020HowDC].
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+# Introduction
+
+Deep neural network has been widely used in many computer vision tasks, yielding significant improvements over conventional approaches since AlexNet [18]. However, image denoising has been one of the tasks in which conventional methods such as BM3D [7] outperformed many early deep learning based ones [5,47,48] until DnCNN [51] outperforms it for synthetic Gaussian noise at the expense of massive amount of noiseless and noisy image pairs [51].
+
+Requiring no clean and/or noisy image pairs, deep image prior (DIP) [42, 43] has shown that a randomly initialized network with hour-glass structure acts as a prior for several inverse problems including denoising, super-resolution, and inpainting with a single degraded image. Although DIP exhibits remarkable performance in these inverse problems, denoising is the particular task that DIP does not perform well, *i.e.*, a single run yields far lower PSNR than BM3D even for synthetic Gaussian noise set-up [42, 43]. Further-
+
+
+
+
+
+Figure 1: Comparison of single image based denoising methods. 'L↓' refers to LPIPS and it is lower the better. 'P↑' refers to PSNR and it is higher the better. Our method denoises an image while preserves rich details; showing the best LPIPS with comparable PSNR to Self2Self (S2S) [30]. Ours shows much better trade-off in PSNR and LPIPS than all other methods including all different ensembling attempts of state of the art (S2S). (Numbers in the circle of S2S denotes number of models in ensemble)
+
+more, for the best performance, one needs to monitor the PSNR (*i.e.*, the ground-truth clean image is required here) and stop the iterations before fitting to noise. Deep Decoder addresses the issue by proposing a strong structural regularization to allow longer iterations for the inverse problems including denoising [15]. However, it yields worse denoising performance than DIP due to low model complexity.
+
+For better use of DIP for denoising without monitoring PSNR with a clean image, we first analyze the model complexity of the DIP by the notion of effective degrees of freedom (DF) [10, 12, 41]. Specifically, the DF quantifies the amount of overfitting (*i.e.*, optimism) of a chosen hypothesis (*i.e.*, a trained neural network model) to the given training data [10]. In other words, when overfitting occurs, the DF increases. Therefore, to prevent the overfitting of the DIP network to the noise, we want to suppress the DF over
+
+iterations. But obtaining DF again requires a clean (ground truth) image. Fortunately, for the Gaussian noise model, there are approximations for DF without using a clean image; Monte-Carlo divergence approximations in Stein's unbiased risk estimator (SURE) (Eqs. [8,](#page-3-0) [9\)](#page-3-1) (DFMC ).
+
+Leveraging SURE and improvement techniques in DIP [\[43\]](#page-9-4), we propose an objective with 'stochastic temporal ensembling (STE),' which mimics ensembling of many noise realizations in a single optimization run. On the proposed objective with the STE, we propose to stop the iteration when the proposed objective function crosses zero. The proposed method leads to much better solutions than DIP and outperforms prior arts for single image denoising. In addition, inspired by PURE formulation [\[20,](#page-8-7) [24\]](#page-8-8), we extend our objective function to address the Poisson noise.
+
+We empirically validate our method by comparing DIP based prior arts for denoising performance in various metrics that are suggested in the literature [\[13\]](#page-8-9) such as PSNR, SSIM and learned perceptual image patch similarity (LPIPS) [\[54\]](#page-9-6) on seven different datasets. LPIPS has been widely used in super resolution literature to complement PSNR, SSIM to measure the recovery power of details [\[21\]](#page-8-10). Since it is challenging for denoiser to suppress noise and preserve details together [\[4\]](#page-8-11), we argue that LPIPS is another appropriate metric to evaluate denoisers. Note that it has not been widely used in denoising literature yet to analyze the denoising performance. Our method not only denoises the images but also preserves rich textual details, outperforming other methods in LPIPS with comparable classic measures including the PSNR and SSIM.
+
+Our contributions are summarized as follows:
+
+- Analyzing the DIP for denoising with effective degrees of freedom (DF) of a network and propose a loss based stopping criterion without ground-truth image.
+- Incorporating noise regularization and exponential moving average by the proposed stochastic temporal ensembling (STE) method.
+- Diverse evaluation in various metrics such as LPIPS, PSNR and SSIM in seven different datasets.
+- Extending our method to Poisson noise.
+
+# Method
+
+**Deep image prior (DIP).** Let a noisy image $\mathbf{y} \in \mathbb{R}^N$ be
+
+
+
+where $\mathbf{x} \in \mathbb{R}^N$ be a noiseless image that one would like to recover and $\mathbf{n} \in \mathbb{R}^N$ be an *i.i.d.* Gaussian noise such that $\mathbf{n} \sim \mathcal{N}(\mathbf{0}, \sigma^2 \mathbf{I})$ where **I** is an identity matrix. Denoising can be formulated as a problem of predicting the unknown x from known noisy observation y. Ulyanov et al. [43] argued that a network architecture naturally encourages to restore the original image from a degraded image y and name it as deep image prior (DIP). Specifically, DIP optimizes a convolutional neural network h with parameter $\theta$ by a simple least square loss $\mathcal{L}$ as:
+
+
+$$\hat{\boldsymbol{\theta}} = \arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\dot{\mathbf{n}}; \boldsymbol{\theta}), \mathbf{y}), \tag{2}$$
+
+where $\dot{\bf n}$ is a random variable that is independent of $\bf v$ . If $\bf h$ has enough capacity (i.e., sufficiently large number of parameters or architecture size) to fit to the noisy image v, the output of model $h(\dot{\mathbf{n}}; \hat{\boldsymbol{\theta}})$ should be equal to $\mathbf{v}$ , which is not desirable. DIP uses the early stopping to obtained the results with best PSNR with clean images.
+
+**Effective degrees of freedom for DIP.** The effective degrees of freedom [10, 41] quantifies the amount of fitting of a model to training data. We analyze the training of DIP by the effective degrees of freedom (DF) in Eq. 3 as a tool for monitoring overfitting to the given noisy image. the DF for the estimator $h(\cdot)$ of x with input y can be defined as follows [14]:
+
+
+$$DF(\mathbf{h}) = \frac{1}{\sigma^2} \sum_{i=1}^n Cov(\mathbf{h}_i(\cdot), \mathbf{y}_i), \tag{3}$$
+
+where $\mathbf{h}(\cdot)$ and $\mathbf{y}$ are a model (e.g., a neural network) and noise image respectively. $\sigma$ is the standard deviation of the noise. $\mathbf{h}_i(\cdot)$ and $\mathbf{v}_i$ indicate the $i^{th}$ element of corresponding vectors. For example, if the input to $\mathbf{h}(\cdot)$ is $\dot{\mathbf{n}}$ and $\mathbf{y}$ is a noisy image, i.e., $h(\dot{n})$ , it is the DF for DIP. Note that $\mathbf{h}(\cdot)$ can take any input and we use $\mathbf{y}$ (instead of $\dot{\mathbf{n}}$ ) for our formulation.
+
+Interestingly, the DF is closely related to the notion of optimism of an estimator h, which is defined by the difference between test error and train error [14,41] as:
+
+$$\rho(\mathbf{h}) = \mathbb{E}\left[\mathcal{L}(\tilde{\mathbf{y}}, \mathbf{h}(\cdot)) - \mathcal{L}(\mathbf{y}, \mathbf{h}(\cdot))\right],\tag{4}$$
+
+where $\mathcal{L}(\cdot)$ is a mean squared error (MSE) loss, $\tilde{\mathbf{y}}$ is another realization from the model (i.e., with different n in Eq. 1) that is independent of y. In [41], it is shown that $\rho(\mathbf{h}) =$ $2\sum_{i=1}^{n} Cov(\mathbf{h}_{i}(\cdot), \mathbf{y}_{i})$ . Thus, combining with Eq. 3, it is straightforward to show that
+
+$$2\sigma^2 \cdot \mathrm{DF}(\mathbf{h}) = \rho(\mathbf{h}). \tag{5}$$
+
+It is challenging to compute the covariance since $h(\cdot)$ is nonlinear (e.g., a neural network), gradually changing in optimization, and the $\rho(\mathbf{h})$ requires many pairs of noisy and clean (ground-truth) images to compute (note that it is an estimate). Here, we introduce a simple approximated degrees of freedom with a single ground-truth and call it as $DF_{GT}$ . We derive the $DF_{GT}$ as following:
+
+$$2\sigma^2 \cdot \mathrm{DF}_{GT}(\mathbf{h}) \approx \mathcal{L}(\mathbf{x}, \mathbf{h}(\cdot)) - \mathcal{L}(\mathbf{y}, \mathbf{h}(\cdot)) + \sigma^2$$
+ (6)
+
+We describe a simple proof of the estimation in the supplementary material.
+
+A large DF implies overfitting to the given input y, which is not desirable. If DIP fits to x, DFGT becomes close to 0. The more the DIP is fitting to y, the larger the DF is. We use the $DF_{GT}$ to analyze the DIP optimization in empirical studies in Sec. 5.1.
+
+To prevent the overfitting of DIP, we try to suppress the DF (Eq. 3) during the optimization without the access of ground-truth clean image x. In Eq. 3, computing the DF is equivalent to the sum of the covariances for each element of the noise image y and the model output $h(\cdot)$ . There are a number of techniques to simply approximate the covariance computation in statistical learning literature such as AIC [1], BIC [36] and Stein's unbiased risk estimator (SURE) [40]. Both AIC and BIC, however, approximate the DF by counting the number of parameters of a model, so for usual over-parameterized deep neural networks, the approximations based on them could be incorrect [3]. Note that $DF_{GT}$ cannot be used for optimizing model because it needs groud-truth clean image x.
+
+Here, we propose to use SURE to suppress the DF by deriving the DIP formulation using the Stein's lemma. The Stein's lemma for a multivariate Gaussian vector y is [\[40\]](#page-8-17):
+
+$$\frac{1}{\sigma^2} \sum_{i=1}^n Cov(\mathbf{h}_i(\mathbf{y}), \mathbf{y}_i) = \mathbb{E}\left[\sum_{i=1}^n \frac{\partial \mathbf{h}_i(\mathbf{y})}{\partial \mathbf{y}_i}\right]. \tag{7}$$
+
+It simplifies the computation of DF from the covariances between y and h(y) to the expected partial derivatives at each point, which is well approximated in a number of computationally efficient ways [\[32,](#page-8-20) [39\]](#page-8-29). Note that the SURE which is denoted as η(h(y), y), consists of Eq. [7](#page-3-3) and the DIP loss (Eq. [2\)](#page-2-3) with a modification of its input (from n˙ to y) as:
+
+$$\eta(\mathbf{h}(\mathbf{y}), \mathbf{y}) = \mathcal{L}(\mathbf{y}, \mathbf{h}(\mathbf{y})) + \underbrace{\frac{2\sigma^2}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_i(\mathbf{y})}{\partial (\mathbf{y})_i}}_{\text{divergence term}} - \sigma^2.$$
+(8)
+
+While the vanilla DIP loss encourages to fit the output of the model h to noisy image y, Eq. [\(8\)](#page-3-0) encourages to approximately fit it to clean image x without access to the x.
+
+However, it is still computationally demanding to use Eq. [8](#page-3-0) as a loss for optimization with any gradient based algorithm due to the divergence term [\[32\]](#page-8-20). A Monte-Carlo approximation for Eq. [8](#page-3-0) in [\[32\]](#page-8-20) can be a remedy to the computation cost, but it introduces a hyper-parameter ϵ that has to be selected properly for the best performance on different network architectures and/or datasets. For not requiring to tune the hyper-parameter ϵ, we employed an alternative Monte-Carlo approximation for the divergence term [\[39\]](#page-8-29) as:
+
+$$\frac{1}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_{i}(\mathbf{y})}{\partial \mathbf{y}_{i}} \approx \frac{1}{N} \tilde{\mathbf{n}}^{T} \mathbf{J}_{\tilde{\mathbf{n}}^{T} \mathbf{h}(\mathbf{y})}, \tag{9}$$
+
+where n˜ is a standard normal random vector, *i.e*., n˜ ∼ N (0, I) and the i th element of the Jacobian Jn˜Th(y) is ∂n˜ T h(y; θ)/∂yi . We denote this 'estimated degrees of freedom by Monte-Carlo' by DFMC and will use it to monitor the DIP optimization without using the PSNR with the clean ground truth images (Sec. [4.2\)](#page-4-2).
+
+To improve the fitting accuracy, DIP suggests several methods including noise regularization, exponential moving average [\[43\]](#page-9-4). We propose 'stochastic temporal ensembling (STE)' for better fitting performance by leveraging these methods to our objective.
+
+Noise regularization on DIP. DIP shows that adding extra temporal noises to the input n˙ of function h(·) at each iteration improves performance for the inverse problems including image denoising [\[43\]](#page-9-4). It is to add a noise vector γ, with γ ∼ N(0, σ2 γ I) to the input of the function at every iteration of the optimization as:
+
+
+$$\hat{\boldsymbol{\theta}} = \arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\dot{\mathbf{n}} + \gamma; \boldsymbol{\theta}), \mathbf{y}), \tag{10}$$
+
+where n˙ is fixed but γ is sampled from Gaussian distribution with zero mean, standard deviation of σγ at every iteration. To estimate the x by Eq. [8,](#page-3-0) we replace the input of the model h(·), n˙ , with noisy image y (from Eq. [3](#page-2-1) to Eq. [7\)](#page-3-3). Interestingly, Eq. [10](#page-3-4) becomes similar to the denoising auto-encoder (DAE), which prevents a model from learning a trivial solution by perturbing output of h [\[46\]](#page-9-13).
+
+Meanwhile, contractive autoencoder (CAE) [\[33\]](#page-8-30) minimizes the Frobenius norm of the Jacobian and SURE and its variants minimize the trace of the Jacobian (Eq. [9\)](#page-3-1) thus suppresses the DF. Since we assume that the different realizations of noise are independent, the off-diagonal elements of the matrix are zero, CAE is equivalent to SURE in terms of suppressing the DF. Alain *et al*. [\[2\]](#page-8-31) later show that the DAE is a special case of the CAE when σγ → 0. We can rewrite the Eq. [10](#page-3-4) by using CAE formulation as:
+
+$$\arg\min_{\boldsymbol{\theta}} \mathcal{L}(\mathbf{h}(\mathbf{y}; \boldsymbol{\theta}), \mathbf{y}) + \sigma_{\gamma}^{2} \left\| \frac{\partial \mathbf{h}(\mathbf{y}; \boldsymbol{\theta})}{\partial \mathbf{y}} \right\|_{F}^{2} + o(\sigma_{\gamma}^{2}), (11)$$
+
+when σγ → 0, where o(σ 2 γ ) is a high order error term from Taylor expansion. Thus, solving this optimization problem is equivalent to penalizing increase of DF. Here, the noise level σγ serves as a hyper-parameter for determining performance and it improves performance of DIP by using multiple level of σz at optimization of DIP. Thus, we further proposed to model σγ as a uniform random variable instead of a empirically chosen hyper-parameter such that
+
+
+$$\sigma_{\gamma} \sim \mathcal{U}(0, b).$$
+ (12)
+
+Exponential moving average. DIP further shows that averaging the restored images obtained in the last iterations improves the performance of denoising [\[43\]](#page-9-4), which we refer to as 'exponential moving average (EMA).' It can be thought as an analogy to the effect of ensembling [\[31\]](#page-8-32).
+
+Stochastic temporal ensembling. Leveraging the noise regularization and the EMA, we propose a method called 'stochastic temporal ensembling (STE)' to improve the fitting performance of DIP loss. Specifically, we modify our formulation (Eq. [8\)](#page-3-0) by allowing two noise observations, y1 for target of MSE loss and y2 for the input of the model, h, instead of one y by setting y1 = y and y2 = y + γ as:
+
+$$\eta(\mathbf{h}(\mathbf{y_2}), \mathbf{y_1}) = \underbrace{\mathcal{L}(\mathbf{h}(\mathbf{y_2}), \mathbf{y_1})}_{\text{data fidelity}} + \underbrace{\frac{2\sigma^2}{N} \sum_{i=1}^{N} \frac{\partial \mathbf{h}_i(\mathbf{y_2})}{\partial (\mathbf{y_2})_i}}_{\text{regularization}} - \sigma^2, \quad (13)$$
+
+where σ is a known noise level of y1 (same as Eq. [1\)](#page-2-2), hi(y2) and (y2)i are the i th element of the vectors of h(y2) and y1, respectively. Interestingly, Eq. [13](#page-3-5) is equivalent to the formulation of extended SURE (eSURE) [\[55\]](#page-9-14), which is shown to be a better unbiased estimator of the MSE with
+
+
+
+Figure 2: Illustration of a solution trajectory of ours and DIP. We consider the problem of reconstructing an image x from a degraded measurement y. DIP finds its optimal stopping point (t4) by early stopping. Ours changes DIP's solution trajectory from black to orange whose stopping point (t5) is defined by a loss value (Sec[.4.2\)](#page-4-2) and is close to noiseless solution (x).
+
+the clean image x. But there are a number of critical differences of ours from [\[55\]](#page-9-14). First, Our method does not require training, while Zhussip *et al*. [\[55\]](#page-9-14) requires training with many noisy images. Because Zhussip *et al*. [\[55\]](#page-9-14) use the fixed instance of γ, there is no effect of regularization from (Eq. [10\)](#page-3-4), which gives reasonable performance gain( See Sec[.5.2\)](#page-5-0). This is our final objective function of DIP that stops automatically by a stopping criterion, described in the following section.
+
+SURE works well if the model h satisfies the smoothness condition, *i.e*., h admits a well-defined second-order Taylor expansion [\[27,](#page-8-18) [38\]](#page-8-21). While a typical learning based denoiser satisfies this smoothness condition [\[38,](#page-8-21)[55\]](#page-9-14), the DIP network 'fits' to a target image (a noisy image in [\[42,](#page-9-3) [43\]](#page-9-4) and an approximate clean image in our objective) and therefore there is no guarantee that the smoothness condition can be satisfied, especially when it has been converged.
+
+We observed that the divergence term in our formulation (Eq. [13\)](#page-3-5) increases at early iterations (*i.e*., before convergence) while it starts to diverge to −∞ at later iterations (*i.e*., after convergence). This observation is consistent in all our experiments. Note that this divergence phenomenon was not reported in [\[27\]](#page-8-18) because the DIP network with the SURE loss did not seem to be fully converged to recover the fine details with insufficient number of iterations. Based on this observation for our proposed objective, we propose 'zero crossing stopping criterion' to stop iteration when our objective function (Eq. [13\)](#page-3-5) deviates from zero.
+
+Solution trajectory. To help understand the difference between our method to DIP in optimization procedure, similar to Fig. 3 in [\[43\]](#page-9-4), we illustrate DIP image restoration trajectory with that of our method in Fig. [2.](#page-4-3) DIP degrades the quality of the restored images by the overfitting. To obtain the solution close to the clean ground truth image, DIP uses early stopping (blue t4). Our formulation has different training trajectory (orange) from DIP (black) and automatically stops the optimization by the zero crossing stopping (orange t4). We argue that the resulting image by our formulation is in general closer to the clean image (blue x) than the solution by DIP, which preserves more high frequency details than the solution by the DIP (Sec. [5.3\)](#page-6-0) thanks to a better target to fit (an approximation of the clean x over a noisy image y and our proposed principled stopping criterion without using ground truth image). We empirically analyze this phenomenon with our proposed DFGT and compare it to DFMC in Sec. [5.1](#page-4-1) and the supplementary material.
+
+As the SURE is limited to Gaussian noise [\[38\]](#page-8-21), there are several attempts to extend it to other types of noises [\[11,](#page-8-22)[20,](#page-8-7) [35\]](#page-8-23). Here, we extend our formulation to Poisson noise as it is a useful model for noise in low-light condition. We modify our formulation (Eq. [13\)](#page-3-5) to use Poisson unbiased risk estimator (PURE) [\[17,](#page-8-33)[20,](#page-8-7)[24\]](#page-8-8) for Poisson noise as follows:
+
+$$\mathcal{L}(\mathbf{h}(\mathbf{y}), \mathbf{y}) - \frac{\zeta}{N} \sum_{i=1}^{N} \mathbf{y}_{i} + \frac{2\zeta}{\epsilon N} (\mathbf{\check{n}} \odot \mathbf{y})^{\mathbf{T}} (\mathbf{h}(\mathbf{y} + \dot{\epsilon}\mathbf{\check{n}}) - \mathbf{h}(\mathbf{y}))), \quad (14)$$
+
+where ˘n is a k-dimensional binary random variable whose element n˘i takes -1 or 1 with probability 0.5 for each, ϵ˙ is a small positive number and ⊙ is a Hadamard product. We empirically validate the Poisson extension in Sec. [5.4.](#page-6-1)
diff --git a/2109.14982/main_diagram/main_diagram.drawio b/2109.14982/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..fd56534b33439a24332f7ee7278a373aef7ffe79
--- /dev/null
+++ b/2109.14982/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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\ No newline at end of file
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+# Introduction
+
+The progress in sensing technologies has significantly expedited the 3D data acquisition pipelines which in turn has increased the availability of large and diverse 3D datasets. With a single 3D sensing device [@lu2006single], one can capture a target surface and represent it as a 3D object, with point clouds and meshes being the most popular representations. Several applications, ranging from virtual reality and 3D avatar generation [@lattas2020avatarme; @potamias2020learning] to 3D printing and digitization of cultural heritage [@pavlidis2007methods], require such representations. However, generally, a 3D capturing device generates thousands of points per second, making processing, visualization and storage of captured 3D objects a computationally daunting task. Often, raw point sets contain an enormous amount of, not only redundant, but also possibly noisy, points with low visual perceptual importance, which results into an unnecessary increase in the storage costs. Thus processing, rendering and editing applications require the development of efficient simplification methods that discard excessive details and reduce the size of the object, while preserving their significant visual characteristics.
+
+Point cloud simplification can be described as a process of reducing the levels-of-detail (LOD) so as to minimise the introduced perceptual error [@cignoni1998comparison]. In contrast to sampling methods, the main objective of point cloud simplification, is the removal or the collapse of particular points that does not significantly affect the visual perceptual quality, in a way that the most salient features are preserved in the simplified point cloud [@luebke2001developer]. In this study, we propose a learnable strategy to remove the least perceptually important points without sacrificing the overall structure of the point cloud. As perceptually important features that should be preserved, we consider points with increased surface curvature, that have been shown to highly correlate with the human perceptual system [@lee2005mesh; @lavoue2009local].
+
+Traditional simplification methods address the task by attempting to solve an optimization problem that minimizes the visual error of the simplified model. Usually, such optimizations are non-convex with high computational cost, where a point importance queue is constructed to sort the 3D points according to their scores [@rossignac1993multi; @hoppe1996progressive; @garland1997surface]. Point cloud simplification methods can be categorized as *mesh-based* and as *point decimation-based*. Mesh-based methods attempt to reconstruct a 3D surface from the point cloud and simplify the generated mesh. In contrast, point decimation-based methods directly select points from the reference point cloud according to their feature scores. Edge contraction remains to date one of the most successful and popular mesh-based methods, since it produces high quality approximations of the input [@garland1997surface; @garland1998simplifying]. Although several approaches proposed parallel GPU computations to reduce the execution time even by 20 times [@decoro2007real; @wang2019fast], the simplification task can be still considered as a computationally hard problem. To this end, it is essential to reduce the computation and time complexity by leveraging neural networks to efficiently simplify point clouds.
+
+In this study, we propose the first, to the best of our knowledge, learnable point cloud simplification method. The proposed method preserves both the salient features as well as the overall structure of the input and can be used for real-time point cloud simplification without any prior surface reconstruction. We also show the limitations of popular distance metrics, such as Chamfer and Haussdorf, to capture salient details of the simplified models and we propose several evaluation criteria that are well-suited for simplification tasks. The proposed method is extensively evaluated in a series of wide range experiments.
+
+The rest of the paper is structured as follows. In Section 2, we succinctly present a summary of related work covering the relevant areas of mesh and point cloud simplification, point cloud sampling as well as learnable graph pooling methods. In Section 3, we present the preliminaries and the details of the proposed methods including the model architecture components, the training procedure, the limitations of uniform distance measures along with the implementation details. Section 4 is dedicated to review and present the evaluation criteria used to measure the performance of the proposed method. Finally, in Section 5, we extensively evaluate our method with a series of qualitative and quantitative experiments. In particular, we report the performance and the execution time of the proposed method using many perceptual and distance measures. In addition, we show that the simplified point clouds can be still identified by pre-trained classifiers. We also qualitatively evaluate the proposed method under noisy and in-the-wild point clouds and establish our findings using a user-study.
+
+# Method
+
+Calculating the local surface properties of an unstructured point cloud is a non-trivial problem. As demonstrated in [@hoppe1992surface; @pauly2002efficient], covariance analysis can be an intuitive estimator of the surface normals and curvature. In particular, considering a neighborhood $\mathcal{N}_i$ around the point $\mathbf{p}_i \in \mathcal{R}^3$ we can define the covariance matrix:
+
+$$\begin{equation}
+ C =\begin{bmatrix}
+ \mathbf{p}_{i_1} - \mathbf{p}_i \\
+ \mathbf{p}_{i_2} - \mathbf{p}_i\\
+ \vdots \\
+ \mathbf{p}_{i_k} - \mathbf{p}_i
+\end{bmatrix}^T \cdot \begin{bmatrix}
+ \mathbf{p}_{i_1} - \mathbf{p}_i \\
+ \mathbf{p}_{i_2} - \mathbf{p}_i\\
+ \vdots \\
+ \mathbf{p}_{i_k} - \mathbf{p}_i
+\end{bmatrix}
+\in \mathbb{R}^{\lvert\mathcal{N}_i\rvert\times\lvert\mathcal{N}_i\lvert}
+\end{equation}$$ where $\mathbf{p}_{i_j} \in \mathcal{N}_i$.
+
+Solving the eigendecomposition of the covariance matrix $C$, we can derive the eigenvectors corresponding to the principal eigenvalues, that define an orthogonal frame at point $\mathbf{p}_i$. The eigenvalues $\lambda_i$ measure the variation along the axis defined by their corresponding eigenvector. Intuitively, the eigenvectors that correspond to the largest eigenvalues span the tangent plane at point $\mathbf{p}_i$, whereas the eigenvector corresponding to the smallest eigenvalue can be used to approximate the surface normal $n_i$. Thus, given that the smallest eigenvalue measures the deviation of point $\mathbf{p}_i$ from the surface, it can be used as an estimate of point curvature. As shown in [@pauly2002efficient], we may define: $$\begin{equation}
+ \kappa(\mathbf{p}_i) = \frac{\lambda_0}{\lambda_0+\lambda_1+\lambda_2}, \quad \lambda_0<\lambda_1<\lambda_2
+\end{equation}$$ as the local curvature estimate at point $\mathbf{p}_i$ which is ideal for tasks such as point simplification. Using the previously estimated curvature at point $\mathbf{p}_i$ we can estimate the mean curvature as the Gaussian weighted average of the curvatures around the neighborhood $\mathcal{N}_i$: $$\begin{equation}
+ \bar{\mathcal{K}}(\mathbf{p}_i) = \frac{\sum\limits_{j \in \mathcal{N}_i} \kappa(\mathbf{p}_j) \exp{(-\|\mathbf{p}_j - \mathbf{p}_i\|^2/h})}{\sum\limits_{j \in \mathcal{N}_i}\exp{(-\|\mathbf{p}_j - \mathbf{p}_i\|^2/h}) }
+\end{equation}$$ where $h$ is a constant defining the radius of the neighborhood. Finally, we can define an estimation of the roughness as the difference between curvature and the mean curvature at point $\mathbf{p}_i$ as: $$\begin{equation}
+ \mathcal{R}(\mathbf{p}_i)=\lvert\kappa(\mathbf{p}_i) -\bar{\mathcal{K}}(\mathbf{p}_i)\rvert
+\end{equation}$$
+
+
+
+
+
+Point cloud simplified using FPS (left) achieves better Chamfer distance (CD) than a point cloud decimated using curvature-preservation methods (right). However, the perceptual similarity scores are better for the latter.
+
+
+The main building block of our architecture is a graph neural network that receives at its input a point cloud (or a mesh) $\mathcal{P}_1$ with $N$ points $\mathbf{p}_i$ and outputs a simplified version $\mathcal{P}_2$ with $M$ points, 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
\ No newline at end of file
diff --git a/2110.06084/paper_text/intro_method.md b/2110.06084/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..919800959b7a4f697023ddb9ef3cdc0cd974ef20
--- /dev/null
+++ b/2110.06084/paper_text/intro_method.md
@@ -0,0 +1,35 @@
+# Introduction
+
+In this study, we analyze linear G-CNNs in a binary classification setting, where hidden layers perform equivariant operations over a finite group $G$, and networks have no nonlinear activation function after their linear group operations. Inputs $\vx_i$ are vectors of dimension $|G|$ (*i.e.,* vectorized group functions $\vx:G \to \mathbb{R}$), and targets $y_i$ are scalars taking the value of either $+1$ or $-1$. Hidden layers in our G-CNNs perform cross-correlation over a group $G$, defined as $$\begin{equation}
+ (\vg \star \vh)(u) = \sum_{v \in G} \vg(uv) \vh(v)
+ \label{eq:cross-correlation}
+\end{equation}$$ where $\vg, \vh:G \to \mathbb{R}$. Note that the above is equivariant to the left action of the group, *i.e.,* if $w \in G$ and $\vg_w'(u) = \vg(wu)$, then $(\vg_w' \star \vh)(u) = (\vg \star \vh)(wu)$. The final layer of our G-CNN is a fully connected layer mapping vectors of length $|G|$ to scalars. We note that this final layer in general will not construct functions that are symmetric to the group operations, as strictly enforcing group invariance in this linear setting will result in trivial outputs (only scalings of the average value of the input). Nonetheless, this model still captures the composed convolutions of G-CNNs, and is similar in construction to many practical G-CNN models, whose earlier G-convolutions still capture useful high-level equivariant features. For instance, the spherical CNN of @cohen2018spherical also has a final fully connected layer.
+
+Analogous to the discrete Fourier transform, there exists a *group* Fourier transform mapping a function into the Fourier basis over the irreps of $G$.
+
+{#fig:correlation width="3.3in"}
+
+::: {#def:group_fourier_transform .definition}
+**Definition 1** (Group Fourier transform). Let $f:G \to \mathbb{C}$. Given a fixed ordering of $G$, let $\ve_u$ be the standard basis vector in $\R^{|G|}$ that is $1$ at the location of $u$ and $0$ elsewhere. Then, $\vf = \sum_{u \in G} f(u) \ve_u$ is the vectorized function $\vf$. Given ${}\widehat{G}$ a complete set of unitary irreps of $G$, let $\rho \in {}\widehat{G}$ be a given irrep of dimension $d_\rho$, $\rho:G \longrightarrow \operatorname{GL}\left(d_\rho,\,\mathbb{C}\right)$[^2]. The group Fourier transform of $f$, $\widehat{f}: \widehat{G} \rightarrow \mathbb{C}$ at a representation $\rho$ is defined as [@terras1999fourier] $$\begin{equation}
+ \widehat{f}(\rho) = \sum_{u \in G} f(u) \rho(u).
+\end{equation}$$ By choosing a fixed ordering of $\widehat{G}$, one can similarly construct $\widehat{\vf}$ as a block-diagonal matrix version of $\widehat{f}$ (as in [4](#fig:correlation){reference-type="ref+label" reference="fig:correlation"}). We define $\mathcal{F}_M$ to be the *matrix* Fourier transform that takes $\vf$ to $\widehat{\vf}$: $$\begin{align}
+% \cF_M f = \sum_{u \in G} f(u) \cF_M (u) = \sum_{u \in G} f(u) \bigoplus_{\rho \in {}\widehat{G}} \rho(u)^{\oplus d_\rho} = \bigoplus_{\rho \in {}\widehat{G}} \widehat{f}(\rho)^{\oplus d_\rho} \;\in\on{GL}\left(|G|, \,\mathbb{C}\right)
+\widehat \vf = \mathcal{F}_M \vf = \bigoplus_{\rho \in {}\widehat{G}} \widehat{f}(\rho)^{\oplus d_\rho} \;\in\operatorname{GL}\left(|G|, \,\mathbb{C}\right).
+\end{align}$$ $\widehat \vf$ or $\mathcal{F}_M \vf$ are shortened notation for the complete Fourier transform. Furthermore, by vectorizing the matrix $\widehat{\vf}$, there is a *unitary* matrix $\mathcal{F}$ taking $\vf$ to $\widehat{\vf}$, analogous to the standard discrete Fourier matrix. We use the following explicit construction of $\mathcal{F}$: denoting $\ve_{[\rho,i,j]}$ as the column-major vectorized basis for element $\rho_{ij}$ in the group Fourier transform, then we can form the matrix $$\begin{equation}
+ \mathcal{F}= \sum_{u \in G} \sum_{\rho \in {}\widehat{G}} \frac{\sqrt{d_\rho}}{\sqrt{|G|}} \sum_{i,j=1}^{d_\rho} \rho(u)_{ij} \ve_{[\rho,i,j]} \ve_u^{T}.
+\end{equation}$$ Intuitively, for each group element $g$, the matrix $\mathcal{F}$ contains all the irrep images $\rho(g)$ 'flattened' into a single column. See [9](#app:gfourier){reference-type="ref+label" reference="app:gfourier"} for further exposition.
+:::
+
+Convolution and cross-correlation are equivalent, up to scaling, to matrix operations after Fourier transformation. For example, for cross-correlation ([\[eq:cross-correlation\]](#eq:cross-correlation){reference-type="ref+label" reference="eq:cross-correlation"}), $\widehat{(g \star h)} (\rho) = {}\widehat{g}(\rho) {}\widehat{h}(\rho)^\dagger$. This simple fact, illustrated in [4](#fig:correlation){reference-type="ref+label" reference="fig:correlation"}, is behind the proofs of our implicit bias results.
+
+
+
+
+A practical G-CNN architecture for D8.
+
+
+
+The linear G-CNN architecture we analyze for D8.
+
+A comparison between a practical G-CNN architecture for the group D8, and its corresponding linear idealization that we will theoretically analyze. D8 is a group of size 8, consisting of all rotations by 90 degrees and reflections. In a practical architecture, as shown in the first panel, the input may be an image, or anything upon which D8 acts; it can be convolved over D8 with respect to a learnable filter, yielding a function on D8 (i.e. a function that takes 8 values). The following layers intersperse D8-convolutions, possibly with several channels, and nonlinearities, before a final fully connected layer yields a scalar output. In contrast, the second panel shows the simplified architecture we analyze here. In particular, there are no non-linearities and only a single channel is used. Furthermore, we assume the input is already a function on D8, i.e. the input is an 8-dimensional vector. (One can think of this input as a fixed featurization of some image convolved over D8 with a fixed filter.)
+
diff --git a/2110.11852/main_diagram/main_diagram.drawio b/2110.11852/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..8f7d44534c4658bf7dcda3f16eae021146e622a7
--- /dev/null
+++ b/2110.11852/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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py/C59/eQZB2zqumZrMtYPTIQWbf7imxFRUjWZSlTaYEYsrFKC1IrqopRKFeKiazLWA3oAhfXcjA983w+I8riEEr8HdC4F96HarzyptwO1HiT0/Kae8V0RCPKYkwyi+2Mxotwu6HTOUqiZMoRiAGC681nbJmEsnV1+ThFAgYzv6c0ouzMSFHdNXeNHDwojulyYowU4pATyy6bFNXSgX05jFCKk3aYfpceP6hoP/WfugDxsH8GXQCeUFHdJSKfeFuPg0+oqJqRvOJteEJFZUWZImRbmlBRLSP5bUtxoKKiFAX3qOg+GzLGkMfarKeAeI8jmQOGVqIlxpwCVtS2zIFjlPEcsqL7OdnAx/vwxnPCjiVYlJZnA4viCGDRvZBrFRYNdGTjvnu2zR64Ex5Ay9GmRb312XDnJMGhjTBusX1oI8KsCvrphU6HA7Y+UYQ8HBBhCUFzIhmNrVySsCRj71LxmfkqSMYCBtFeSUbCUSyDR8zhzgdkjKG+Xl3CxGLdiADeyM/5gEypAn5RcL15fEBSxkMvkF8s4TzHJ79IOFJrgE7AcT53kdngFwvijV8sICynlo4NP+0wAZtcBQBbIEv8YtOLu+cXBYV44xeJhPZ8OfxiQYYBhFd+kcQhf0bGL/YOqDJSGMuhRd8lmvKLY4OAbX4RliPiFwXXW4j74oBx/fCLNFX2Ubg/sIaPwmR+R/wiU46AXxRcb8G14kBjXYcrqQ1+saQh+MVdqdyOb3wiasgvnv6mTsNnCaHVNb9YwIUMz/xiPxMLzy/urMFWVIT8ImXVu0Ck3lilBakVVcUolCvFxC/SODSgazoZMOclb+QasxXrmrXJyYDMvUKfDEjjEKfO8GTAOHzR4GRAeK+wJwNSCSUsJthDd2VSA/aAYoJ25iOcOjqEPaiM2nbGvJeObubNYUTShbTD9MGiH96LyohaV9kFMN3+WXQBHCFp4r2kJHWfjArl6CsT7yU2kldGpZcmJ95LmCuCA7alnIu4TryXyEh+2xKrgDE2uQ7eq88eizHksTbrKZkcfT8LecIlmdQx75XHKFq65L16jxn4eB/eeE5wsMR7FeRseK9cQkmcDgdUURj77tm2BO5umppBdVI7uxtMU92dDljCHVAEpwPC0MV6/kUuIYRe3W5kjEN4Ph0wZ+XMC9zAtCSw2fndwDR3KQCmTCwCl5lG+mSJ+pPsSTfVar5dvgxvrxBVMMCjrrCLELwVlcxV0urRXG7ld4FmpbDLitSsLrf0Y80auxkRElS9tBWxTyv2IdqRFb/U69v5tlrPP4Z/aNJzkqssoeYIbk9UshG0M7mqYDXFL/Xj+2SXJhDB2diI6MUyqbgDvBx4BzODC+l3tfERgResmvVtVT00D/+6fKqaP3x/fpjaRJudBjdM4LUJ1G8gar9RcLe8e/pYUtz9n7GSh1zTk1VGctazs1mWMgBgiNTTgpUDmvH5pV4970bn5qGaZ0qTv6rNumo/+dRWTmPKkcq+wiZBUXETsEFwMqGkG8RZ4vo5xPva1uSR2C94MgJdtW6/bH65a3/53oygTQ1/wCFHjam/riljf+nUwIQGLznKhKADRbxIwU6Dk5AbXANDaZ5IjMLUWRX4nZrrKC47K4WarAPRX1dwAao0tJ3FaXrvPZNF/VjUhQlldpPyq5cFMpmhjTI4w7GWxNq83NT19vjyds79e72o2iv+Dw==
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+# Introduction
+
+In the literature of time series analysis, AR and ARMA models are most commonly used to fit and to predict time series data. For example, given the historical traffic volume $z_1, z_2, ..., z_{t-1}$ on a specific road, the government wants to predict future traffic $z_t, z_{t+1}, ...$. Generally speaking, each observation $z_t$ can depend on all previous ones in a nonlinear manner and the relationship may vary as $t$ differs. AR and ARMA models are linear and assume a formulation where coefficients only depend on the *time lag* between the target and the past observation, shared across different target timestamp $t$. Thus, they are simple, parsimonious and were proved to be useful in many applications.
+
+Time series is a sequence of random variables and the white noise sequence plays a necessary role in time series models as the source of randomness. In the main text, we refer to the white noise term as the additive error because it is an addend in the model equation.
+
+Formally, a $p$th order autoregressive model, a.k.a., an AR($p$) model, $\{Z_t\}$ satisfies $$\begin{equation*}
+Z_t = \theta_0 + \phi_1Z_{t-1} + \phi_2Z_{t-2} + \cdots + \phi_pZ_{t-p} + a_t,
+\end{equation*}$$ where $p\geq 0$ is an integer, $\phi$'s are real parameters and $\{a_t\}$ is a white noise sequence. An autoregressive-moving-average model of orders $p$ and $q$, a.k.a., an ARMA$(p, q)$ model, satisfies $$\begin{equation*}
+Z_t = \theta_0 + \phi_1Z_{t-1} + \phi_2Z_{t-2} + \cdots + \phi_pZ_{t-p} + a_t - \theta_1a_{t-1} - \theta_2 a_{t-1} - \cdots - \theta_q a_{t-q},
+\end{equation*}$$ where $p, q\geq 0$ are integers, $\theta$'s are real parameters for the MA part. *Invertibility* is a property of time series models to characterize whether the information sequence $\{a_t\}$ can be recovered from past observations, and it is always required in time series applications. If an ARMA model is invertible, then it has an *AR representation* $$\begin{equation*}
+Z_t = \pi_1 Z_{t-1} + \pi_2 Z_{t-2} + \cdots + a_t ,
+\end{equation*}$$ which takes the form of an AR($\infty$) model, where the infinitely many $\pi$'s can be fully determined by $p+q$ parameters, i.e., $\phi$'s and $\theta$'s. In the main text, we claim that ARMA model simplifies AR and give an example using ARMA$(1, 1)$; see Eq. (7). We are referring to the fact that any invertible ARMA model has an AR representation, whose parameters can be fully determined by the (much fewer) parameters of the ARMA model.
+
+
+
+Visualization of a layer in the Shared-Lag modification of DenseNet in Eq. (9).
+
+
+Figure [4](#fig:shared1x1){reference-type="ref" reference="fig:shared1x1"} visualizes the structure of a modified DenseNet layer following Eq. (9), where cubes represent feature tensors, rectangles are convolutions annotated with kernel size and output channels, and $\oplus$ denotes elementwise addition. Numbers inside dashed circles correspond to the following notes:
+
+1. Operations inside the large dashed rectangle represents the $2$nd layer in a dense block.
+
+2. This 1x1 convolution is shared among layers within a stage.
+
+3. Dense connections are implemented by this storage area of past layers' output, and $0$ is used as a placeholder.
+
+4. This arrow represents a virtual \"conveyor belt\" indicating that the feature maps are always ordered from the most recent layer to the most distant layer.
+
+$\textrm{Conv3}^t$ and $\textrm{Conv1}_{0}^t(\cdot)$ in Eq. (9) are represented by the unshared 3x3 and 1x1 convolutions (white rectangles), and a combination of $\textrm{Conv1}_1, \textrm{Conv1}_2, ..., \textrm{Conv1}_{15}$ is represented by the shared 1x1 convolution (colored rectangle with note 2). Feature maps from previous layers are ordered such that Eq. (9) holds. The Shared-Ordinal variant of DenseNet can be similarly implemented by adjusting the ordering (see note 3 above) and disabling the \"conveyor belt\" (see note 4 above).
+
+Figure 2 in the main paper shows the $L_1$ norm of the weights of the shared 1x1 convolutions. And we observe the following main difference between the two modes of weight sharing:
+
+- When weights are shared based on lag, *the most recent layer* corresponds to the largest weight, and the $L_1$ norm decays as lag increases.
+
+- When weights are shared according to the ordinal number of the layers, *layers closer to the input* correspond to larger weights, and the first few layers have similar weights.
+
+We also claimed that the quick decaying pattern in the plot for Shared-Lag could be exponential decay, and we provide numerical support below. We fit exponential functions to the curves in Figure 2 and use the $R$-squared metric to compare goodness-of-fit. $R^2 \in (0, 1)$ is a metric that can be interpreted as the proportion of variance explained by a model, i.e., a fitted curve. $R^2$ values for the three curves in the Share-Lag subplot are $0.95, 0.86, 0.90$, while that for Shared-Ordinal are $0.77, 0.91, 0.87$, implying poorer fit.
+
+This subsection shows that the RLA module in Eq. (11) implements layer aggregation as in Eq. (1). And under a linearity assumption, Eq. (11) can also be shown to have the additive form in Eq. (2).
+
+Recall the formulation of RLA mechanism in Eq. (11), $$\begin{equation*}
+h^t = g^t (h^{t-1}, x^{t-1})\hspace{5mm}\text{and}\hspace{5mm}
+x^t=f^t (h^{t-1}, x^{t-1}).
+\end{equation*}$$ Recursively substitute $h^s = g^s (h^{s-1}, x^{s-1})$ for $s = t-1, t-2, ...$ into the first equation, we have $$\begin{align*}
+h^t & = g^t (h^{t-1}, x^{t-1}) \\
+& = g^t (g^{t-1} (h^{t-2}, x^{t-2}), x^{t-1}) \\
+& = \cdots,
+\end{align*}$$ which is a function of $x^{t-1}, x^{t-2}, ..., x^0$ and a constant $h^0$. Thus, Eq. (1) is satisfied.
+
+If we further assume that there exist functions $g_1^s$ and $g_2^s$ such that $g^s$ and $g_1^s$ satisfy $g^s (h^{s-1}, x^{s-1}) = g_1^s (h^{s-1}) + g_2^s (x^{s-1})$ and $g_1^s(u + v) = g_1^s(u) + g_1^s(v)$ for all $s$, we have $$\begin{align*}
+h^t & = g_1^t (h^{t-1}) + g_2^t (x^{t-1}) \\
+& = g_1^t (g_1^{t-1} (h^{t-2}) + g_2^{t-1} (x^{t-2})) + g_2^t (x^{t-1}) \\
+& = g_1^t (g_1^{t-1} (h^{t-2})) + g_1^t (g_2^{t-1} (x^{t-2})) + g_2^t (x^{t-1}) \\
+& = \cdots,
+\end{align*}$$ which is a summation over transformed $x^{t-1}, x^{t-2}, ..., x^0$ with a constant $g_1^t(g_1^{t-1}(\cdots g_1^1(h^0) ))$.
+
+# Method
+
+Consider the RLA module given by Figure 3 with update $$\begin{equation*}
+h^t=g_2[g_1(y^t)+h^{t-1}]\hspace{5mm}\text{and}\hspace{5mm}
+x^t=y^t+x^{t-1},
+\end{equation*}$$ where $y^t=f_1^t [\text{Concat}(h^{t-1}, x^{t-1})]$ and $g_1, g_2$ are the shared 1x1 and 3x3 convolutions. As discussed in the main text, ResNets have a layer aggregation interpretation, i.e., $x^t$ is an aggregation of the residual information $y^t$. According to the ablation study in Sections 4.4 and [7.4](#sec:ablation_cifar){reference-type="ref" reference="sec:ablation_cifar"}, it is preferable for the RLA module to perform an aggregation of the residual information $y^t$ instead of the already aggregated $x^t$. Thus, in the following, we show that $h^t$ is an aggregation of $y^t$ instead of $x^t$.
+
+When nonlinearities are ignored, the shared convolution $g_2$ can be distributed to the two terms, i.e., $$\begin{equation*}
+h^t = g_2(h^{t-1}) + g_2 \circ g_1(y^t),
+\end{equation*}$$ Furthermore, recursively applying the above equation, we have $$\begin{align}
+h^t = \, & g_2(h^{t-1}) + g_2 \circ g_1(y^t) \nonumber \\
+= \, & g_2(g_2(h^{t-2}) + g_2 \circ g_1(y^{t-1})) + g_2 \circ g_1(y^t) \nonumber \\
+= \, & g_2^2(h^{t-2}) + g_2^2 \circ g_1(y^{t-1}) + g_2 \circ g_1(y^t) \nonumber \\
+& \vdots \nonumber \\
+= \, & \sum_{k=1}^t g_2^k \circ g_1 (y^{t-k+1}) + g_2^t(h^0), \label{eq:RLA-LA}
+\end{align}$$ where $\circ$ denotes the composition of convolution functions, and with a slight abuse of notation, the composition of the same function $k$ times is denoted as its $k$-th power, e.g., $$\begin{matrix} g_2^k = & \underbrace{ g_2 \circ g_2 \circ \cdots \circ g_2 } \\ & k \text{ times} \end{matrix} ,$$ not to be confused with time varying functions where the superscript denotes the time index. Thus, the RLA hidden feature maps $h^t$ are aggregations of previous residual information.
+
+Moreover, the patterns of layer aggregation in ResNets and the RLA module in Eq. [\[eq:RLA-LA\]](#eq:RLA-LA){reference-type="eqref" reference="eq:RLA-LA"} are very similar to the AR($\infty$) and ARMA($1, 1$) models introduced in Sections 3.2 and [6.2](#sec:time-series){reference-type="ref" reference="sec:time-series"}. Because for the proposed RLA module in Figure 3, the convolutions $g_l^t$ in Eq. (2) are solely determined by the two shared convolutions $g_2$ and $g_1$.
diff --git a/2111.14893/main_diagram/main_diagram.drawio b/2111.14893/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..b4789d48a89edb98402b8085f1e4b19894f6dfc4
--- /dev/null
+++ b/2111.14893/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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+# Introduction
+
+With the recent advances in dense prediction computer vision problems [\[17,](#page-8-0) [25,](#page-8-1) [38,](#page-9-0) [43,](#page-9-1) [47,](#page-9-2) [54,](#page-9-3) [57,](#page-9-4) [64,](#page-10-0) [65,](#page-10-1) [68,](#page-10-2) [72,](#page-10-3) [73\]](#page-10-4), where the aim is to produce pixel-level predictions (*e.g*. semantic and instance segmentation, depth estimation), the interest of the community has started to shift towards the more ambitious goal of learning multiple of these problems jointly by multi-task learning (MTL) [\[7\]](#page-8-2). Compared to the standard single task learning (STL) that focuses on learning an individual model for each task, MTL aims at learning a single model for multiple tasks with a better efficiency and generalization tradeoff while sharing information and computational resources across them.
+
+Recent MTL dense prediction methods broadly focus on designing MTL architectures [\[4,](#page-8-3) [5,](#page-8-4) [19,](#page-8-5) [24,](#page-8-6) [37,](#page-9-5) [39,](#page-9-6) [44,](#page-9-7) [49,](#page-9-8) [58,](#page-9-9) [60,](#page-9-10) [66,](#page-10-5) [76–](#page-10-6)[78\]](#page-10-7) that enable effective sharing of information across tasks and improving the MTL optimization [\[12,](#page-8-7)[13,](#page-8-8)[21,](#page-8-9) [23,](#page-8-10)[30,](#page-8-11)[33,](#page-9-11)[34,](#page-9-12)[37,](#page-9-5)[51,](#page-9-13)[67\]](#page-10-8) to balance the influence of each task-
+
+
+
+Figure 1. Multi-task partially supervised learning. We look at the problem of learning multiple tasks from partially annotated data (b) where not all the task labels are available for each image, which generalizes over the standard supervised learning (a) where all task labels are available. We propose a MTL method that employs a shared feature extractor (fϕ) with task-specific heads (hψ) and exploits label correlations between each task pair by mapping them into a *joint pairwise task-space* and penalizing inconsistencies between the provided ground-truth labels and predictions (c).
+
+specific loss function and to prevent interference between the tasks in training. We refer to [\[59\]](#page-9-14) for a more comprehensive review. One common and strong assumption in these works is that each training image has to be labelled for all the tasks (Fig. [1\(](#page-0-0)a)). There are two main practical limitations to this assumption. First, curating multi-task image datasets (*e.g*. KITTI [\[20\]](#page-8-12) and CityScapes [\[15\]](#page-8-13)) typically involves using multiple sensors to produce ground-truth labels for several tasks, and obtaining all the labels for each image requires very accurate synchronization between the sensors, which is a challenging research problem by itself [\[61\]](#page-9-15). Second, imagine a scenario where one would like to add a new task to an existing image dataset which is already annotated for another task and obtaining the ground-truth labels for the new task requires using a different sensor (*e.g*. depth camera) to the one which is used to capture the original data. In this case,
+
+labelling the previously recorded images for the new task will not be possible for many visual scenes (*e.g*. uncontrolled outdoor environments). Such real-world scenarios lead to obtaining partially annotated data and thus ask for algorithms that can learn from such data.
+
+In this paper, we look at a more realistic and general case of the MTL dense prediction problem where not all the task labels are available for each image (Fig. [1\(](#page-0-0)b)) and we call this setting *multi-task partially supervised learning*. In particular, we assume that each image is at least labelled for one task and each task at least has few labelled images and we would like to learn a multi-task model on them. A naive way of learning from such partial supervision is to train a multi-task model only on the available labels (*i.e*. by setting the weight of the corresponding loss function to 0 for the missing task labels). Though, in this setting, the MTL model is trained on all the images thanks to the parameter sharing across the tasks, it cannot extract the task-specific information from the images for the unlabelled tasks. To this end, one can extend existing single-task semi-supervised learning methods to MTL by penalizing the inconsistent predictions of images over multiple perturbations for the unlabelled tasks (*e.g*. [\[14,](#page-8-14)[29,](#page-8-15)[32,](#page-9-16)[36,](#page-9-17)[56\]](#page-9-18)). While this strategy ensures consistent predictions over various perturbations, it does not guarantee consistency across the related tasks.
+
+An orthogonal information that has recently been used in MTL is cross-task relation [\[40,](#page-9-19)[50,](#page-9-20)[69\]](#page-10-9) which aims at producing consistent predictions across task pairs. Unfortunately existing methods are not directly applicable for learning from partial supervision, as they require either each training image to be labelled with all the task labels [\[50,](#page-9-20) [69\]](#page-10-9) or cross-task relations that can be analytically derived [\[40\]](#page-9-19). In our setting, compared to [\[40,](#page-9-19)[50,](#page-9-20)[69\]](#page-10-9), there are fewer training images available with ground-truth labels of each task pair and thus it is harder to learn the relationship. In addition, unlike [\[40\]](#page-9-19), we focus on the general setting where one task label cannot be accurately obtained from another (*e.g*. from semantic segmentation to depth) and hence learning exact mappings between two task labels is not possible.
+
+Motivated by these challenges, we propose a MTL approach that shares a feature extractor between tasks and also learns to relate each task pair in a learned *joint pairwise task-space* (illustrated in Fig. [1\(](#page-0-0)c)), which encodes only the shared information between them and does not require the ill-posed problem of recovering labels of one task from another one. There are two challenges to this goal. First, a naive learning of the joint pairwise task-spaces can lead to trivial mappings that take all predictions to the same point such that each task produces artificially consistent encodings with each other. To this end, we regulate learning of each mapping by penalizing its output to retain high-level information about the input image. Second, the computational cost of modelling each task pair relation can get exponentially expensive with the number of tasks. To address this challenge, we use a single encoder network to learn all the pairwise-task mappings, however, dynamically estimate its weights by conditioning them on the target task pair.
+
+The main contributions of our method are as following. We propose a new and practical setting for multi-task dense prediction problems and a novel MTL model that penalizes cross-task consistencies between pairs of tasks in joint pairwise task-spaces, each encoding the commonalities between pairs, in a computationally efficient manner. We show that our method can be incorporated to several architectures and significantly outperforms the related baselines in three standard multi-task benchmarks.
+
+# Method
+
+Let x ∈ R 3×H×W and y t ∈ R Ot×H×W denote an H × W dimensional RGB image and its dense label for task t respectively, where Ot is the number of output channels for task t. Our goal is to learn a function yˆ t for each task t that accurately predicts the ground-truth label y t of previously unseen images. While such a task-specific function can be learned for each task independently, a more efficient design is to share most of the computations across the tasks via a common feature encoder, convolutional neural network fϕ : R 3×H×W → R C×H′×W′ parameterized by ϕ that takes in an image and produces a C feature maps, each with H′×W′ resolution, where typically H′ < H and W′ < W. In this setting, fϕ is followed by multiple task-specific decoders hψt : R C×H′×W′ → R Ot×H×W , each with its own taskspecific weights ψ t that decodes the extracted feature to predict the label for the task t, *i.e*. yˆ t (x) = hψt ◦ fϕ(x) (Fig. [2\(](#page-3-0)a)).
+
+Let D denote a set of N training images with their corresponding labels for K tasks. Assume that for each training image x, we have ground-truth labels available only for some tasks where we use T and U to store the indices of labeled and unlabelled tasks respectively, where |T | + |U| = K, U = ∅ indicates all labels available for x and T = ∅ indicates no labels available for x. In this paper, we focus on the partially annotated setting, where each image is labelled at least for one task (|T | ≥ 1) and each task at least has few labelled images.
+
+A naive way of learning yˆ t for each task on the partially annotated data D is to jointly optimize its parameters on the labelled tasks as following:
+
+
+$$\min_{\phi,\psi} \frac{1}{N} \sum_{n=1}^{N} \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t(\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t), \tag{1}$$
+
+where n is the image index and L t is the task-specific differentiable loss function. We denote this setting as the (vanilla) MTL. Here, thanks to the parameter sharing through the feature extractor, its task-agnostic weights are learned on all the images. However, the task-specific weights ψ t are trained only on the labeled images.
+
+A common strategy to exploit such information from unlabeled tasks is to formulate the problem in a semisupervised learning (SSL) setting. Recent successful SSL techniques [\[2,](#page-8-21)[53\]](#page-9-27) focus on learning models that can produce consistent predictions for unlabelled images when its input is perturbed in various ways.
+
+
+$$\min_{\phi,\psi} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t(\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t) + \frac{1}{|\mathcal{U}_n|} \sum_{t \in \mathcal{U}} L_u(e_r(\hat{y}^t(\boldsymbol{x}_n)), \hat{y}^t(e_r(\boldsymbol{x}_n))) \right), \tag{2}$$
+
+where Lu is the unsupervised loss function and er is a geometric transformation (*i.e*. cropping) parameterized by the random variable r (*i.e*. bounding box location). In words, for the unsupervised part, we apply our model to the original input x and also its cropped version er(x), and then we also crop the prediction corresponding to the original input er(ˆy t (xn)) before we measure the difference between two by using Lu. Note that we are aware of more sophisticated task-specific SSL methods for semantic segmentation [\[42,](#page-9-28) [45\]](#page-9-29), depth estimation [\[22,](#page-8-22) [31\]](#page-8-23), however, combining them for multiple tasks, each with different network designs and learning formulations is not trivial and here we focus on one SSL strategy that uses one perturbation type (*i.e*. random cropping) and Lu (*i.e*. mean square error) can be applied to several tasks.
+
+While optimizing Eq. [\(2\)](#page-2-0) allows learning both taskagnostic and task-specific weights on the labeled and unlabelled data, it does not leverage cross-task relations, which can be used to further supervise unlabelled tasks. Prior works [\[40,](#page-9-19) [69\]](#page-10-9) define the cross-task relations by a mapping function ms→t for each task-pair (s, t) which maps the prediction for the source task s to target task t labels. The mapping function in [\[40\]](#page-9-19) is analytical based on the assumption that target task labels can be analytically computed from source labels. While such analytical relations is possible
+
+
+
+Figure 2. Illustration of our method for multi-task partially supervised learning. Given an image, our method uses a shared feature extractor $f_{\phi}$ taking in the input image and task-specific decoders $(h_{\psi^s}$ and $h_{\psi^t})$ to produce predictions for all tasks (a). We compute the supervised loss $L_t$ for labelled task. Besides, we regularize the cross-task consistency $L_{ct}$ between the unlabelled task's prediction $\hat{y}^s$ and the labelled task's ground-truth $y^t$ in a joint space for the unlabelled task (b). To learn the cross-task consistency efficiently, we propose to use a shared mapping function whose output is conditioned on the task-pair (c) and regularize the learning of mapping function using the feature from $f_{\phi}$ to prevent trivial solution.
+
+only for certain task pairs, each mapping function in [69] is parameterized by a deep network and its weights are learned by minimizing $L_{ct}(m^{s \to t}(\boldsymbol{y}^s), \boldsymbol{y}^t)$ , where $L_{ct}$ is cross-task function that measures the distance between the mapped source labels and target labels. There are two limitations to this method in our setting. First the training set has limited labelled number of images for both source and target tasks $(\boldsymbol{y}^s)$ and $\boldsymbol{y}^t$ . Second learning such pairwise mappings accurately is not often possible in our case, as the labels of one task can only be partially recovered from another task (e.g.) semantic segmentation to depth estimation). Note that this ill-posed problem can be solved accurately when strong prior knowledge about the data is available.
+
+To employ cross-task consistency to our setting, we map each task pair (s,t) to a lower-dimensional joint pairwise task-space where only the common features of both tasks are encoded (Fig. 2(b)). Formally, each pairwise task-space for (s,t) is defined by a pair of mapping functions, $m_{\vartheta_s^{st}}: \mathbb{R}^{O^s \times H \times W} \to \mathbb{R}^D$ and $m_{\vartheta_t^{st}}: \mathbb{R}^{O^t \times H \times W} \to \mathbb{R}^D$ parameterized by $\vartheta_s^{st}$ and $\vartheta_t^{st}$ respectively. The cross-task consistency can be incorporated to Eq. (1) as following:
+
+$$\min_{\phi, \psi, \vartheta} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_n|} \sum_{t \in \mathcal{T}_n} L^t (\hat{y}^t(\boldsymbol{x}_n), \boldsymbol{y}_n^t) + \frac{1}{|\mathcal{U}_n|} \sum_{s \in \mathcal{U}_n, t \in \mathcal{T}_n} L_{ct} (m_{\vartheta_s^{st}} (\hat{y}^s(\boldsymbol{x}_n)), m_{\vartheta_t^{st}} (\boldsymbol{y}_n^t)) \right),$$
+(6)
+
+where $L_{ct}$ is cosine distance (i.e. $L_{ct}(\mathbf{a}, \mathbf{b}) = 1 - (\mathbf{a} \cdot \mathbf{b})/(|\mathbf{a}||\mathbf{b}|)$ ). In words, along with the MTL optimization, Eq. (3) minimizes the cosine distance between the embeddings of the unlabelled task prediction $\hat{y}_s$ and the annotated task label $y^t$ in the joint pairwise task space. Here $m_{\vartheta_s^t}$
+
+and $m_{\vartheta_t^{st}}$ are not necessarily equal to allow for treating the mapping from predicted and ground-truth labels differently. Note that one can also include the semi-supervised term $L_u$ in Eq. (3). However we empirically found that it does not bring any tangible performance gain when used with the cross-task term $L_{ct}$ .
+
+There are two challenges to learn non-trivial pairwise mapping functions in a computationally efficient way. First the number of pairwise mappings to learn quadratically grows with the number of tasks. Although the mapping functions are only used in training, it can still be computationally expensive to train many of them jointly. In addition, learning an accurate mapping for each task-pair can be challenging in case of limited labels. Second the mapping functions can simply learn a trivial solution such that each task is mapped to a fixed point (*e.g.* zero vector) in the joint space.
+
+Conditional joint task-pair mapping. To address the first challenge, as shown in Fig. 2(c), we propose to use a task-agnostic mapping function $\bar{m}_{\vartheta}$ with one set of parameters $\vartheta$ whose output is conditioned both on the input task (s or t) and task-pair (s, t) through an auxiliary network $(a_{\theta})$ . Concretely, let A denote a variable that includes the input task (s or t) and target pair (s,t) for a pairwise mapping which in practice we encode with an asymmetric $K \times K$ dimensional matrix by setting the corresponding entry to 1 (i.e. A[s,t] = 1 or A[t,s] = 1) and the other entries to 0. Note that the diagonal entries are always zero, as we do not define any self-task relation. Let $\bar{m}_{\vartheta}$ be a multi-layer network and $h_i$ denote a M channel feature map of its i-th layer for which the auxiliary network $a_{\theta}$ , parameterized by $\theta$ , takes in A and outputs two M-dimensional vectors $a_{\theta,i}^c$ and $a_{\theta,i}^b$ . These vectors are applied to transform the feature
+
+map $h_i$ in a similar way to [46] as following:
+
+$$\boldsymbol{h}_i \leftarrow a_{\theta,i}^c(A) \odot \boldsymbol{h}_i + a_{\theta,i}^b(A)$$
+
+where $\odot$ denote a Hadamard product. In words, the auxiliary network alters the output of the task-agnostic mapping function $\bar{m}_{\vartheta}$ based on A. For brevity, we denote the conditional mapping from s to (s,t) as $m^{s \to st}$ which is a function of $\bar{m}_{\vartheta}$ and $a_{\theta}$ and hence parameterized with $\vartheta$ and $\theta$ .
+
+We implement each $a_i^c$ and $a_i^b$ as an one layer fully-connected network. Hence, given the light-weight auxiliary network, the computational load for computing the conditional mapping function, in practice, does not vary with the number of task-pairs. Finally, as the dimensionality of each task label vary -e.g. while $O^t$ is 1 for depth estimation and $O^t$ equals to number of categories in semantic segmentation –, we use task-specific input layers and pass each prediction to the corresponding one before feeding it to the joint pairwise task mapping. In the formulation, we include these layers in our mapping $\bar{m}_\vartheta$ and explain their implementation details in Sec. 4.
+
+**Regularizing mapping function.** To avoid learning trivial mappings, we propose a regularization strategy (Fig. 2) that encourages the mapping to retain high-level information about the input image. To this end, we penalize the distance between the output of the mapping function and a feature vector that is extracted from the input image. In particular, we use the output of the task-agnostic feature extractor $f_{\phi}(x)$ in the regularization. Now we can add the regularizer to the formulation in Eq. (3):
+
+
+$$\min_{\phi,\psi,\vartheta,\theta} \frac{1}{N} \sum_{n=1}^{N} \left( \frac{1}{|\mathcal{T}_{n}|} \sum_{t \in \mathcal{T}_{n}} L^{t}(\hat{y}^{t}(\boldsymbol{x}_{n}), \boldsymbol{y}_{n}^{t}) + \frac{1}{|\mathcal{U}_{n}|} \sum_{s \in \mathcal{U}_{n}, t \in \mathcal{T}_{n}} L_{ct}(m^{s \to st}(\hat{y}^{s}(\boldsymbol{x}_{n})), m^{t \to st}(\boldsymbol{y}_{n}^{t})) + R(f_{\phi}(\boldsymbol{x}_{n}), m^{s \to st}(\hat{y}_{s}(\boldsymbol{x}_{n}))) + R(f_{\phi}(\boldsymbol{x}_{n}), m^{t \to st}(\boldsymbol{y}_{n}^{t})) \right), \tag{4}$$
+
+where $f_{\phi}(x)$ is the feature from feature encoder $f_{\phi}$ , R is the loss function and we use the cosine similarity loss for R in this work.
+
+Alternative mapping strategies. Here we discuss two different mapping strategies to exploit cross-task consistency proposed in [69] and their adoption to our setting. As both require learning a mapping from one task's groundtruth label to another one and we have either no or few images with both groundtruth labels, here we approximate them by learning mappings from prediction of one task to another task's groundtruth. In the first case, one can substitute our cross-consistency loss and regularization terms with $L_{ct}(m^{s \to t}(\hat{y}^s(\boldsymbol{x})), \boldsymbol{y}^t)$ in Eq. (4), which is denoted as Direct-Map. In the second case, we replace our
+
+terms with $L_{ct}(m^{s \to t}(\hat{y}^s(\boldsymbol{x})), m^{s \to t}(\boldsymbol{y}^s))$ that maps both the groundtruth $\boldsymbol{y}^s$ and predicted labels $\hat{\boldsymbol{y}}^s$ and minimize their distance in task t's label space. We denote this setting as Perceptual-Map and compare to them in Sec. 4.
+
+Alternative loss and regularization strategies. Alternatively, our cross-consistency loss and regularization terms can be replaced with another loss function only that does not allow for learning of trivial mappings. One such loss function is contrastive loss where one can define the predictions for two tasks on the same image as a positive pair (i.e. $m^{s\to st}(\hat{y}^s(\boldsymbol{x}_i))$ and $m^{t\to st}(\boldsymbol{y}_i^t)$ and on different images as a negative pair (i.e. $m^{s \to st}(\hat{y}^s(\boldsymbol{x}_i))$ and $m^{t \to st}(\boldsymbol{y}_i^t)$ ), and penalize when the distance from the positive one is bigger than the negative one. We denote this setting as *Contrastive-Loss*. Another method which also employs positive and negative pairs involves using a discriminator network. The discriminator (a convolutional neural network) takes in positive and negative pairs and predicts their binary labels, while the parameters of the MTL network and mapping functions are alternatively optimized. We denote this setting as Discriminator-Loss and compare to the alternative methods in Sec. 4.
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+# Introduction
+
+The task of *Question Matching (QM)* aims to identify the question pairs that have the same meaning, and it has been widely used in many applications, e.g., community question answering and intelligent customer services, etc. Though neural QM models have shown compelling performance on various datasets, including Quora Question Pairs (QQP) (Iyer et al., 2017), LCQMC (Liu et al., 2018), BQ (Chen et al., 2018) and AFQMC1, neural models are often not robust to adversarial examples, which means that the neural models predict unexpected outputs given just a small perturbations
+
+on the inputs. As the example 1 in Tab. 1 shows, a model might not distinguish the minor difference ("面 noodles") between the two sentences, and thus predicts the two questions semantically equivalent.
+
+Recently, it attracts a lot of attentions from the research community to deal with the robustness issues of neural models on various NLP tasks, such as question matching, natural language inference and machine reading comprehension. Early works examine the robustness of neural models by creating a certain types of artificial adversarial examples (Jia and Liang, 2017; Alzantot et al., 2018; Ren et al., 2019; Jin et al., 2020), and involving human-and-model-in-the-loop to create dynamic adversarial examples (Nie et al., 2020; Wallace et al., 2019). Further studies discover that a few types of superficial cues (i.e. shortcuts) in the training data, are learned by the models and hence affect the model robustness (Gururangan et al., 2018; Mc-Coy et al., 2019; Lai et al., 2021). Besides, several studies try to improve the robustness of the neural models by adversarial data augmentation (Min et al., 2020) and data filtering (Bras et al., 2020). All these efforts lead us to better find and fix the robustness issues to some extends.
+
+However, there are several limitations in previous studies. First, the analysis and evaluation in previous work focus on just one or a few types of adversarial examples or shortcuts, but we need normative evaluation (Linzen, 2020; Ettinger, 2020; Phang et al.). The goal of the normative evaluation is not to fool a system by exploiting its particular weaknesses, but using systemically controlled datasets to comprehensively evaluate the basic linguistic capabilities of the models in a diverse way. Checklist (Ribeiro et al., 2020) and Textflint (Wang et al., 2021) are great attempts of normative evaluation. However, it is not clear that if the effects of the artificial adversarial methods on artificial examples are still shown on natural texts from real-world applications (Morris et al., 2020). Some other works
+
+\* Equal contribution. The work was done when Hongyu Zhu was doing internship at Baidu.
+
+<sup>1It is from Ant Technology Exploration Conference (ATEC) Developer competition, which is no longer available.
+
+manually perturb the examples to construct natural examples, but the manual perturbations is time consuming and costly [\(Gardner et al.,](#page-8-6) [2020\)](#page-8-6). Moreover, to the best of our knowledge, there are few Chinese datasets for QM robustness evaluation.
+
+Towards this end, we create a open-domain Chinese dataset namely DuQM contains natural questions with linguistic perturbation for evaluating the robustness of QM models. (1) By *linguistic*, we mean this systematically controlled dataset provides a detailed breakdown of evaluation by linguistic phenomenon. As shown in Tab. [1,](#page-2-0) there are 3 categories and 13 subcategories with 32 linguistic perturbation in DuQM, which enables us to evaluate the model performance by each category instead of just a single metric. (2) By *natural*, we mean all the questions in DuQM are natural and issued by the users in a commercial search engine. This design can help us to properly evaluate the progress of a model's robustness on natural texts rather than artificial texts which may not preserve semantics and introduce grammatical errors.
+
+The contributions of this paper can be summarized as follows:
+
+- We construct a Chinese dataset namely DuQM that contains linguistically perturbed natural questions from a commercial search engine. It is a systemically controlled dataset to test the basic linguistic capabilities of the models in a diverse way. (see Sec. [2](#page-1-0) and Sec. [3\)](#page-4-0)
+- Our experimental results show that 3 characteristics of DuQM: (1) DuQM is challenging, and has better discrimination power to distinguish the models that perform comparably on other datasets (see Sec. [4.2\)](#page-6-0). (2) The detailed breakdown of evaluation by linguistic phenomena in DuQM helps diagnose the advantages and disadvantages of different models (see Sec. [4.3\)](#page-6-1). (3) Extensive experiment shows that the effect of artificial adversarial examples does not work on natural texts of DuQM. DuQM can help us properly evaluate the models' robustness. (see Sec. [4.4\)](#page-7-0).
+
+The remaining of this paper is organized as follows. Sec. [2](#page-1-0) describes the 3 categories and 13 subcategories with 32 linguistic perturbation in DuQM. Sec. [3](#page-4-0) gives the construction process of DuQM. In Sec. [4,](#page-5-0) we conduct experiments to demonstrate 3 characteristics of DuQM. We conclude our work in Sec. [5.](#page-8-7)
+
+The design of DuQM is aimed at a detailed breakdown of evaluation by linguistic phenomenon. Hence, we create DuQM by introducing a set of linguistic features that we believe are important for model diagnosis in terms of linguistic capabilities. Basically, 3 categories of linguistic features are used to build DuQM, i.e., lexical features (see Sec. [2.1\)](#page-1-1), syntactic features (see Sec. [2.2\)](#page-3-0), and pragmatic features (see Sec. [2.3\)](#page-4-1). We list 3 categories, 13 subcategories with 32 operations of perturbation in Tab. [1.](#page-2-0) The detailed descriptions of all categories are given in this section.
+
+Lexical features are associated with vocabulary items, i.e. words. As a word is the smallest independent but meaningful unit of speech , an operation on a single word may change the meaning of the entire sentence. It is a basic but crucial capability for models to understand word and perceive word-level perturbations. To provide a fine-grained evaluation for model's capability of lexical understanding, we further consider 6 subcategories:
+
+Part of Speech. Parts of speech (POS), or word classes, describe the part a word plays in a sentence. DuQM considers 6 POS in Chinese grammar, including noun, verb, adjective, adverb, numeral and quantifier, which are content words that carry most meaning of a sentence. In this subcategory, we aim to test the models' understanding of related but not identical words with different POSs. As the example 1 in Tab. [1](#page-2-0) [2](#page-1-2) shows, inserting only one noun "面 *noodles*" makes the sentence meaning different. Furthermore, in this subcategory we provides a set of examples focusing on phrase-level perturbations to check model's capability on understanding word groups that act collectively as a single part of speech (see example 11).
+
+Named Entity. Different from common nouns that refer to generic things, a named entity (NE) is a proper noun which refers to a specific realworld object. The close relation to world knowledge makes NE ideal for observing models' understanding of the meaning of names and background knowledge about entities. In DuQM, we include *Named Entity* an independent subcategory to test the model's behavior of named entity recognition, and focus on 4 types of NE most commonly seen,
+
+2All examples discussed in this section are presented in Column *Example and Translation* of Tab. [1.](#page-2-0)
+
+
+
+| Category | Subcategory | Perturbation Operation | Label #Y / #N | BERT base | ERNIE base | RoBERTa base | MacBERT base | RoBERTa large | MacBERT large | Examples and Translation |
+|-------------------|---------------------------------|---------------------------|------------------|--------------|--------------------|-----------------|-----------------|------------------|------------------|-------------------------------------------------------------------------------------------------------------|
+| | | insert n. | -/539 | 41.4±3.4 | 40.8±2.1 | 43.0±0.7 | 41.4±2.5 | 45.4±4.1 | 37.3±2.4 | E1: 鸡蛋怎么炒好吃 /鸡蛋 面 怎么炒好吃 how to fry eggs / how to fry egg noodles |
+| | | insert v. | -/131 | | 39.4±0.4 33.8±2.6 | 37.4±2.0 | 35.9±2.7 | 39.9±3.1 | 29.5±3.8 | E2: 梦到西红柿 / 梦到 摘 西红柿 dream of tomatoes / dream of picking tomatoes |
+| | | insert adj. | -/458 | 23.5±1.9 | 19.2±3.7 | 26.9±4.4 | 23.9±4.2 | 18.1±2.4 | 10.4±2.1 | E3: 有哪些类型的app / 有哪些类型的 移动 app what are types of apps / what are types of mobile apps |
+| | | insert adv. | -/302 | 3.7±0.5 | 4.2±0.5 | 3.8±0.6 | 4.4±1.2 | 5.8±1.5 | 3.1±1.1 | E4: 为什么打嗝 / 为什么 老 打嗝 why burp / why always burp |
+| | Part of | replace n. | -/702 | 86.6±0.3 | 86.7±0.1 | 88.3±0.3 | 88.8±1.2 | 89.4±1.6 | 87.8±0.7 | E5: 申请美国 绿卡 流程 /申请美国 签证 流程 U.S. green card application process / U.S. visa application process |
+| | Speech | replace v. | -/466 | 71.7±1.1 | 77.6±0.8 | 76.9±0.4 | 76.5±1.2 | 81.0±1.6 | 81.5±2.2 | E6: 为什么 下蹲 膝盖疼 /为什么 下跪 膝盖疼 why knee pain when squatting / why knee pain when kneeling |
+| | | replace adj. | -/472 | 74.3±2.1 | 80.0±1.0 | 77.6±0.7 | 81.6±0.5 | 82.7±1.1 | 82.7±1.6 | E7: 耳朵出血 严重 吗 / 耳朵出血 正常 吗 is the ear bleeding serious / is the ear bleeding normal |
+| | | replace adv. | -/188 | 19.1±6.1 | 19.3±4.4 | 16.3±3.8 | 23.9±4.6 | 59.0±4.0 | 56.2±2.0 | E8: 为什么会 经常 头晕 /为什么会 有点 头晕 why regularly feel dizzy / why slightly feel dizzy |
+| | | replace num. | -/1116 | 83.2±1.4 | 91.4±0.4 | 85.9±1.8 | 87.2±0.9 | 88.1±0.5 | 91.9±1.1 | E9: 血压 130 /100高吗 / 血压 120 /100高吗 is blood pressure 130 /100 high / is blood pressure 120 /100 high |
+| | | replace quantifier | -/22 | 30.3±6.9 | 25.7±5.2 | 33.3±2.6 | 34.9±2.6 | 27.3±0.0 | | 34.8±10.5 E10: 一 束 花多少钱 /一 枝 花多少钱 how much is a bunch of flower / how much is a flower |
+| | | replace phrases | -/197 | | 98.0±0.0 98.1±0.2 | 96.6±0.3 | 97.8±0.5 | 97.8±0.2 | 97.5±0 | E11: 如何 提高自己的记忆力 / 如何 增加自己的实力 how to improve my memory / how to increase my strength |
+| Lexical Feature | | replace loc. | -/458 | | 96.0±0.6 95.7±0.2 | 95.4±0.4 | 95.0±0.4 | 94.7±0.4 | 94.5±0.5 | E12: 山西 春节习俗 /陕西 春节习俗 Shanxi spring festival customs / Shannxi spring festival customs |
+| | Named | replace org. | -/264 | | 94.9±0.2 94.3±0.6 | 91.2±1.4 | 93.4±0.7 | 93.5±0.3 | 93.8±0.1 | E13: 北京邮电大学 附近酒店 /南京邮电大学 附近酒店 hotels near BUPT / hotels near NJUPT |
+| | Entity | replace person | -/468 | 90.3±1.3 | 91.0±0.9 | 88.7±1.6 | 91.4±1.6 | 92.3±1.3 | 93.2±1.1 | E14: 陈龙 的妻子 /成龙 的妻子 wife of Long Chen / wife of Jackie Chan |
+| | | replace product | -/170 | 83.7±2.6 | 88.2±2.1 | 82.4±6.9 | 83.3±0.3 | 86.0±1.7 | 88.8±4.4 | E15: iphone 6 多少钱 /iphone6x 多少钱 how much is iphone 6 / how much is iphone6x |
+| | Synonym | replace n. | 405/- | 51.1±1.1 | 59.7±1.3 | 59.7±2.2 | 60.7±2.0 | 63.3±3.1 | 71.6±4.0 | E16: 猕猴桃 的功效 /奇异果 的功效 health benefits of Chinese gooseberry / health benefits of Kiwi |
+| | | replace v. | 372/- | 80.0±0.9 | 81.1±1.6 | 82.5±0.0 | 83.2±1.2 | 84.0±2.0 | 88.1±1.4 | E17: 什么果汁可以 减肥 / 什么果汁可以 减重 what juice can lose weight / what juice can slim |
+| | | replace adj. | 453/- | 75.7±1.3 | 77.3±1.1 | 78.8±2.5 | 74.8±0.5 | 79.4±3.4 | 88.5±1.3 | E18: 有趣 搞笑的广告词 / 幽默 搞笑的广告词 funny advertising words / humerous advertising words |
+| | | replace adv. | 26/- | | 98.7±2.1 100.0±0.0 | 100.0±0.0 | 100.0±0.0 | 100±0.0 | | 100.0±0.0 E19: 总是 想睡觉是为什么 /老是 想睡觉是为什么 why always want to sleep / why repeatedly want to sleep |
+| | Antonym | replace adj. | -/305 | 50.6±3.4 | 69.6±2.9 | 65.0±1.5 | 73.1±4.3 | 91.7±2.3 | 90.7±2.3 | E20: 什么水果脂肪 低 / 什么水果脂肪 高 what fruit is low in fat / what fruit is high in fat |
+| | | negate v. | -/153 | 69.9±9.6 | 88.9±1.3 | 84.8±2.9 | 93.3±1.3 | 88.4±0.9 | 91.4±3.4 | E21: 为什么宝宝哭 /为什么宝宝 不 哭 why baby cries / why baby doesn't cry |
+| | Negation | negate adj. | -/139 | 73.1±8.5 | 84.2±1.2 | 82.7±1.4 | 88.0±1.5 | 88.0±2.9 | 89.4±1.0 | E22: 为什么苹果是红的 /为什么苹果 不是 红的 why apple is red / why apple is not red |
+| | | neg.+antonym | 59/- | 29.9±2.5 | 34.4±2.5 | 39.0±1.7 | 31.1±2.5 | 40.7±1.7 | 53.6±0.9 | E23: 激动 怎么办 /无法 平静 怎么办 what to do if too excited / what to do if can't calm down |
+| | Temporal | insert | -/120 | 26.6±2.1 | 29.1±2.1 | 33.1±0.9 | 41.7±3.3 | 47.5±5.4 | 33.6±8.5 | E24: 北京会下雨吗 /北京 明天 会下雨吗 will it rain in Beijing / will it rain in Beijing tomorrow |
+| | word | replace | -/114 | 44.1±6.1 | 67.8±2.6 | 55.0±0.5 | 53.8±1.3 | 70.4±6.1 | 78.6±5.8 | E25: 昨天 下雪 了 吗 /明儿 会下雪吗 was it snow yesterday / will it snow tomorrow |
+| | Symmetry | swap | 533/- | | 97.3±0.4 98.0±0.1 | 95.2±1.7 | 95.9±0.7 | 93.3±0.9 | 92.5±1.9 | E26: 鱼 和 鸡蛋 能一起吃吗 / 鸡蛋 和 鱼 能一起吃吗 can I eat fish with egg / can I eat egg with fish |
+| | Asymmetry | swap | -/497 | 14.5±2.0 | 18.3±3.7 | 26.8±3.2 | 26.4±2.5 | 52.0±4.6 | | 49.1±10.8 E27: 北京 到 上海 航班 /上海 到 北京 航班 Beijing to Shanghai flights / Shanghai to Beijing flights |
+| Syntactic Feature | Negative Asymmetry | swap + negate | 49/- | | 47.6±3.4 37.4±7.7 | 44.2±1.1 | 25.8±3.1 | 23.1±6.7 | 29.9±1.9 | E28: 男人 比 女人 更 高 吗 / 女人 比 男人 更 矮 吗 are men taller than women / are women shorter than men |
+| | Voice | insert passive word | 94/37 | 76.8±1.4 | 72.5±0.0 | 77.4±0.9 | 74.0±0.7 | 85.2±1.4 | 74.8±2.2 | E29: 梦见狗咬左腿 /梦见 被 狗咬左腿 dreamed of being bitten by a dog / dreamed of being bitten by a dog |
+| | Misspelling | replace | 468/- | | 68.0±2.0 65.1±0.2 | 64.2±0.6 | 65.0±2.3 | 63.5±1.8 | 63.2±1.6 | E30: 什么 纹身 适合我 / 什么 文身 适合我 what tattoo suits me / what tatoo suits me |
+| Pragmatic Feature | Discourse Particle (Simple) | insert or replace | 213/- | 98.7±0.5 | 98.4±0.2 | 98.6±0.5 | 99.2±0.2 | 99.5±0.0 | 99.8±0.2 | E31: 人为什么做梦 /那么 人为什么做梦 why people dream / so why people dream |
+| | Discourse Particle (Complex) | insert or replace | 131/- | 46.5±0.6 | 56.2±2.0 | 64.1±2.0 | 61.6±1.6 | 65.1±3.4 | 68.4±0.3 | E32: 附近最好的餐厅 / 求助我旁边 哪家餐厅 最好吃 ? best restaurant nearby / heeelp!!! which restaurant is best in my area ? |
+| Total | 13 | 32 | 2803/7318 - | | | | | | | - |
+
+Table 1: Categories of DuQM (described in Sec. [2\)](#page-1-0) and performance of 6 models on DuQM (discussed in Sec. [4\)](#page-5-0). Bold face and underlined indicate the first and second highest accuracy for each testing scenario.
+
+i.e., location, organization, person and product. Example 12 is a search query and its perturbation on NE. The two named entities, "山西 *Shanxi*" and "陕西 *Shaanxi*", are similar at character level but denote two different locations. We expect that the models can capture the subtle difference.
+
+**Synonym.** A synonym is a word or phrase that means exactly or nearly the same as another word or phrase in a given language. This subcategory aims to test whether models can identify two semantically equivalent questions whose surface forms only differ in a pair of synonyms. As in example 16, the two sentences differ only in two words, both of which refer to Kiwifruit, so they have the same meaning.
+
+Antonym. In contrast to synonyms, antonyms are words within an inherently incompatible binary relationship. This subcategory examines model's capability on distinguishing words with opposed meanings. We mainly focus on adjective's opposite, e.g., "高high" and "低low" (see example 20).
+
+Negation. Negation is another way to express contradiction. To negate a verb or an adjective in Chinese, we normally put a negative before it, e.g., "不not" before "哭cry" (example 21), "不是not" before "红的red" (example 22). The negative before the verb or the adjective negates the statement. It is an effective way to analyze model's basic skill of figuring out the contradictory meanings even there is only a minor change.
+
+Moreover, we include some equivalent paraphrases with negation in this subcategory. In example 23, "无法平静can't calm down" is the negative paraphrase of "激动excited", so that the paraphrase sentence is equivalent to the positive sentence. We believe that a robust QM system should be able to recognize this kind of paraphrase question pairs.
+
+**Temporal Word.** Temporal reasoning is the relatively higher-level linguistic capability that allows the model to reason about a mathematical timeline. Unlike English, verbs in Chinese do not have morphological inflections. Tenses and aspects are expressed either by temporal noun phrases like " $\mathcal{F}$ tomorrow" (examples 24) or by aspect particles like " $\mathcal{F}$ le", which indicates the completion of an action (examples 25). This subcategory focuses on the temporal distinctions and helps us evaluate the models' temporal reasoning capability.
+
+While single word sense is important to question meaning, how words composed together into a whole also affects sentence understanding. We believe the relations among words in a sentence is important information for models to capture, so we focus on several types of syntactic features in this category. We pre-define 4 linguistic phenomena that we believe is meaningful to locate model's strength and weakness, and describe them here.
+
+Symmetry. Sometimes paraphrases can be generated by only swapping the two conjuncts around in a structure of coordination. As shown in example 26, "鱼fish" and "鸡蛋egg" are joined together by the conjunction "和and", which have the symmetric relation to each other. Even if we swap them around, the sentence meaning will not change. We name this subcategory Symmetry, with which we aim to explore if a model captures the inherent dependency relationship between words.
+
+Asymmetry. Some words (such as "和and") denote symmetric relations, while others (for example, preposition "到to") denote asymmetric. Example 27 shows a sentence pair in which the word before the preposition "到to" is an adverbial and the word after it is the object. Swapping around the adverbial and the object of the prepositional phrase will definitely leads to a nonequivalent meaning. If a model performs well only on subcategory Symmetry or Asymmetry, it may rely on shortcuts instead of the understanding of the syntactic information. Negative Asymmetry. To further explore the syntactic capability of QM model, DuQM includes a set of test examples which consider both syntactic asymmetry and antonym, and we name this category Negative Asymmetry. In example 28, the asymmetric relation between "男人men" and "女 人women" and the opposite meaning of "高taller" and "矮shorter" resolve to an equivalent meaning. With this subcategory, we can better explore model's capability of inferring more complex syntactic structure.
+
+**Voice.** Another crucial syntactic capability of models is to differentiate active and passive voices. In Chinese, the most common way to express the passive voice is using Bei-constructions which feature an agentive case marker "被bei". The subject of a Bei-construction is the patient of an action, and the object of the preposition "被bei" is the agent. Compared to Fig.1(a), the additional "被bei" and the change of word order of "猫cat" and "狗dog"
+
+
+
+What to do if dog is bitten by cat (c) Passive voice non-paraphrase question.
+
+Figure 1: The dependency relations of active voice and passive voice questions.
+
+in Fig[.1\(b\)](#page-4-3) convert the sentence from active to passive voice, but the two sentences have the same meaning. If we further change the word order from Fig[.1\(b\)](#page-4-3) to Fig[.1\(c\),](#page-4-4) the sentence still uses passive voice but has different meaning.
+
+Passive voice is not always expressed with an overt "被*bei*". Sometimes a sentence without any passive marker is still in passive voice. In example 29, although the first sentence is without "被*bei*", it expresses the same meaning as the second one. There are a set of active-passive examples in this category, which are effective to evaluate model's performance on active and passive voices.
+
+Lexical items ordered by syntactic rules are not all that make a sentence mean what it means. Context, or the communicative situation that influence language use, has a part to play. We include some pragmatic features in DuQM so as to observe whether models are able to understand the contextual meaning of sentences.
+
+Misspelling. Misspellings are quite often seen by search engines and question-answering systems, which are mostly unintentional. Models should have the capability to capture the true intention of the questions with spelling errors to ensure the robustness. In example 30, despite the misspelled word "文身*tatoo*" the two questions mean the same, In some real world situations, models should understand misspellings appropriately. For example,
+
+
+
+Figure 2: Construction process of DuQM.
+
+when users search a query but type in misspelling, a robust model will still give the correct result.
+
+Discourse Particle. Discourse particles are words and small expressions that contribute little to the information the sentence convey, but play some pragmatic functions such as showing politeness, drawing attention, smoothing utterance, etc. As in example 32, the word "求助*help*" is used to draw attention and bring no additional information to the sentence. Whether using these little words do not change the sentence meaning. It is necessary to a model to identify the semantic equivalency when such words are used.
+
+We design DuQM as a *diverse* and *natural* corpus. The construction process of DuQM is divided into 4 steps and illustrated in Fig. [2.](#page-4-5) Firstly, we preprocess the source questions to obtain their linguistic knowledge, which will be used to perturb the source texts. Then we pair the source and perturbed question as an example. The examples' naturalness is reviewed by human evaluators. At last, the examples are annotated manually and DuQM is finally constructed. We introduce the construction details in the following:
+
+Linguistic Preprocessing. We collect a large number of source questions from the search query log of a commercial search engine. All the source questions are natural and then we perform several linguistic preprocessings on them: named entity recognition, POS tagging, dependency parsing, and word importance analysis. The linguistic knowledge about the source questions we obtained in this step will be used for perturbation.
+
+Perturbation. We conduct different perturbation
+
+operations for different subcategories. In general, we perturb the sentences in 3 ways:
+
+- replace: replace a word with another word, e.g., for category *Synonym*, we replace one word with its synonym;
+- insert : insert an additional word, e.g., for category *Temporal word*, we insert temporal word to the source question;
+- swap: swap two words. This operation is only used in *Syntactic Feature*.
+
+The perturbations of all categories are listed in column *Perturbation Operation* of Tab. [1,](#page-2-0) and the perturbation details will be given in Appendix [A.](#page-10-0) Naturalness Review. To ensure the generated sentences are natural, we examine their appearances in the search log and only retain the sentences which have been entered into the search engine.
+
+Annotation. The source question and generated question are paired together as an example. Then the examples are evaluated by evaluators from our internal data team. They need to evaluate whether the examples are fluent, grammatically correct, and correctly categorized. The low-quality examples are discarded and the examples with inappropriate categories are re-classified.
+
+Then the question pairs are annotated by the linguistic experts from our internal data team. Semantically equivalent question pairs are positive examples, and inequivalent pairs are negative. The annotators are required a approval rate higher than 99% for at least 1,000 prior tasks. Each example is annotated by three annotators, and the examples will be tagged with the label which more than 2 annotators choose. To further ensure the annotation quality, 10% of the annotated examples are selected randomly and reviewed by another senior linguistic expert and and if the review accuracy is lower than 95%, the annotation linguistic experts need to re-annotate all the examples until the accuracy is higher than 95%. Since all annotators are linguistic experts from our internal data team instead of crowd-sourcing, we do not need to use IAA to measure the annotation quality. Generally, only 0.002% (20/10,167) generated examples are not fluent or not grammatically correct, and only 0.02% (195/10,121) generated examples are re-annotated manually. The overall annotation process is illustrated in Fig. [3.](#page-11-0)
+
+Eventually, we generate 10,121 examples for DuQM. The class distribution of all categories are
+
+
+
+| | | Length | # | | | |
+|-------------|------|--------|-------|-------|--------|--|
+| Category | q | q' | Y | N | All | |
+| Lexical | 8.58 | 8.89 | 1,315 | 6,784 | 8,099 | |
+| Syntactic | 9.86 | 9.89 | 678 | 532 | 1,210 | |
+| Pragmatic | 8.73 | 9.03 | 812 | 0 | 812 | |
+| Avg / Total | 8.74 | 8.90 | 2,805 | 7,316 | 1,0121 | |
+
+Table 2: Data statistics of DuQM.
+
+
+
+| Model | LCQMCtest | DuQM | 4 |
+|----------|-----------|----------|-------|
+| BERTb | 87.1±0.1 | 66.6±0.6 | -20.5 |
+| ERNIEb | 87.3±0.1 | 69.8±0.3 | -17.5 |
+| RoBERTab | 87.2±0.4 | 69.5±0.1 | -17.7 |
+| MacBERTb | 87.4±0.3 | 70.3±0.6 | -17.1 |
+| RoBERTal | 87.7±0.1 | 73.8±0.3 | -13.9 |
+| MacBERTl | 87.6±0.1 | 73.8±0.5 | -13.8 |
+
+Table 3: Accuracy(%) on LCQMCtest and DuQM. b indicates base, and l indicates large.
+
+given in Tab. [1.](#page-2-0) Additional data statistics are provided in Tab. [2.](#page-5-1)
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
\ No newline at end of file
diff --git a/2201.00520/paper_text/intro_method.md b/2201.00520/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..4cf9675dd96435e2ac011a2f749d27c84744af93
--- /dev/null
+++ b/2201.00520/paper_text/intro_method.md
@@ -0,0 +1,117 @@
+# Introduction
+
+Transformer [\[34\]](#page-11-0) is originally introduced to solve natural language processing tasks. It has recently shown great potential in the field of computer vision [\[12,](#page-10-0)[26,](#page-11-1)[36\]](#page-11-2). The pioneer work, Vision Transformer [\[12\]](#page-10-0) (ViT), stacks multiple Transformer blocks to process non-overlapping image patch (*i.e.* visual token) sequences, leading to a convolutionfree model for image classification. Compared to their CNN counterparts [\[18,](#page-10-1)[19\]](#page-10-2), Transformer-based models have larger receptive fields and excel at modeling long-range dependencies, which are proved to achieve superior performance in the regime of a large amount of training data and
+
+
+
+Figure 1. Comparison of DAT with other Vision Transformer models and DCN in CNN model. The red star and the blue star denote the different queries, and masks with solid line boundaries denote the regions to which the queries attend. In a data-agnostic way: (a) ViT [\[12\]](#page-10-0) adopts full attention for all queries. (b) Swin Transformer [\[26\]](#page-11-1) uses partitioned window attention. In a datadependent way: (c) DCN [\[9\]](#page-10-3) learns different deformed points for each query. (d) DAT learns shared deformed points for all queries.
+
+model parameters. However, the superfluous attention in visual recognition is a double-edged sword, and has multiple drawbacks. Specifically, the excessive number of keys to attend per query patch yields high computational cost and slow convergence, and increases the risk of overfitting.
+
+In order to avoid excessive attention computation, existing works [\[6,](#page-10-4) [11,](#page-10-5) [26,](#page-11-1) [36,](#page-11-2) [43,](#page-11-3) [49\]](#page-11-4) have leveraged carefully designed efficient attention patterns to reduce the computation complexity. As two representative approaches among them, Swin Transformer [\[26\]](#page-11-1) adopts window-based local
+
+\*Equal contribution.
+
+†Corresponding author.
+
+attention to restrict attention in local windows, while Pyramid Vision Transformer (PVT) [\[36\]](#page-11-2) downsamples the key and value feature maps to save computation. Though effective, the hand-crafted attention patterns are data-agnostic and may not be optimal. It is likely that relevant keys/values are dropped, while less important ones are still kept.
+
+Ideally, one would expect that the candidate key/value set for a given query is flexible and has the ability to adapt to each individual input, such that the issues in hand-crafted sparse attention patterns can be alleviated. In fact, in the literature of CNNs, learning a deformable receptive field for the convolution filters has been shown effective in selectively attending to more informative regions on a datadependent basis [\[9\]](#page-10-3). The most notable work, Deformable Convolution Networks [\[9\]](#page-10-3), has yielded impressive results on many challenging vision tasks. This motivates us to explore a deformable attention pattern in Vision Transformers. However, a naive implementation of this idea leads to an unreasonably high memory/computation complexity: the overhead introduced by the deformable offsets is quadratic *w.r.t* the number of patches. As a consequence, although some recent work [\[7,](#page-10-6) [46,](#page-11-5) [54\]](#page-11-6) have investigated the idea of deformable mechanism in Transformers , none of them have treated it as a basic building block for constructing a powerful backbone network like the DCN, due to the high computational cost. Instead, their deformable mechanism is either adopted in the detection head [\[54\]](#page-11-6), or used as a preprocessing layer to sample patches for the subsequent backbone network [\[7\]](#page-10-6).
+
+In this paper, we present a simple and efficient deformable self-attention module, equipped with which a powerful pyramid backbone, named *Deformable Attention Transformer* (DAT), is constructed for image classification and various dense prediction tasks. Different from DCN that learns different offsets for different pixels in the whole feature map, we propose to learn a few groups of query agnostic offsets to shift keys and values to important regions (as illustrated in Figure [1\(](#page-0-0)d)), based on the observation in [\[3,](#page-9-0) [52\]](#page-11-7) that global attention usually results in the almost same attention patterns for different queries. This design both holds a linear space complexity and introduces a deformable attention pattern to Transformer backbones. Specifically, for each attention module, reference points are first generated as uniform grids, which are the same across the input data. Then, an offset network takes as input the query features and generates the corresponding offsets for all the reference points. In this way, the candidate keys/values are shifted towards important regions, thus augmenting the original self-attention module with higher flexibility and efficiency to capture more informative features.
+
+To summarize, our contributions are as follows: we propose the first deformable self-attention backbone for visual recognition, where the data-dependent attention pattern endows higher flexibility and efficiency. Extensive experiments on ImageNet [\[10\]](#page-10-7), ADE20K [\[51\]](#page-11-8) and COCO [\[25\]](#page-11-9) demonstrate that our model outperforms competitive baselines including Swin Transformer consistently, by a margin of 0.7 on the top-1 accuracy of image classification, 1.2 on the mIoU of semantic segmentation, 1.1 on object detection for both box AP and mask AP. The advantages on small and large objects are more distinct with a margin of 2.1.
+
+# Method
+
+We first revisit the attention mechanism in recent Vision Transformers. Taking a flattened feature map x ∈ R N×C as the input, a multi-head self-attention (MHSA) block with M heads is formulated as
+
+$$q = xW_q, \ k = xW_k, \ v = xW_v, \tag{1}$$
+
+$$z^{(m)} = \sigma(q^{(m)}k^{(m)\top}/\sqrt{d})v^{(m)}, m = 1, \dots, M,$$
+ (2)
+
+$$z = \operatorname{Concat}\left(z^{(1)}, \dots, z^{(M)}\right) W_o, \tag{3}$$
+
+where σ(·) denotes the softmax function, and d = C/M is the dimension of each head. z (m) denotes the embedding output from the m-th attention head, q (m) , k(m) , v(m) ∈ R N×d denote query, key, and value embeddings respectively. Wq, Wk, Wv, Wo ∈ R C×C are the projection matrices. To build up a Transformer block, an MLP block with two linear transformations and a GELU activation is usually adopted to provide nonlinearity.
+
+With normalization layers and identity shortcuts, the l-th Transformer block is formulated as
+
+$$z'_{l} = MHSA(LN(z_{l-1})) + z_{l-1},$$
+ (4)
+
+$$z_l = \text{MLP}\left(\text{LN}(z_l')\right) + z_l',\tag{5}$$
+
+where LN is Layer Normalization [\[1\]](#page-9-2).
+
+Existing hierarchical Vision Transformers, notably PVT [\[36\]](#page-11-2) and Swin Transformer [\[26\]](#page-11-1) try to address the challenge of excessive attention. The downsampling technique of the former results in severe information loss, and the shiftwindow attention of the latter leads to a much slower growth of receptive fields, which limits the potential of modeling large objects. Thus a data-dependent sparse attention is required to flexibly model relevant features, leading to deformable mechanism firstly proposed in DCN [\[9\]](#page-10-3). However, simply implementing DCN in Transformer models is a non-trivial problem. In DCN, each element on the feature map learns its offsets individually, of which a 3 × 3 deformable convolution on an H ×W ×C feature map has the space complexity of 9HW C. If we directly apply the same mechanism in the attention module, the space complexity will drastically rise to NqNkC, where Nq, Nk are the number of queries and keys and usually have the same scale as the feature map size HW, bringing approximately a biquadratic complexity. Although Deformable DETR [\[54\]](#page-11-6) has managed to reduce this overhead by setting a lower number of keys with Nk = 4 at each scale and works well as a detection head, it is inferior to attend to such few keys in a backbone network because of the unacceptable loss of information (see detailed comparison in Appendix). In the meantime, the observations in [\[3,](#page-9-0)[52\]](#page-11-7) have revealed that different queries have similar attention maps in visual attention models. Therefore, we opt for a simpler solution with shared shifted keys and values for each query to achieve an efficient trade-off.
+
+Specifically, we propose deformable attention to model the relations among tokens effectively under the guidance of the important regions in the feature maps. These focused regions are determined by multiple groups of deformed sampling points which are learned from the queries by an offset network. We adopt bilinear interpolation to sample features from the feature maps, and then the sampled features are fed to the key and value projections to get the deformed keys and values. Finally, standard multi-head attention is applied to attend queries to the sampled keys and aggregate features from the deformed values. Additionally, the locations of deformed points provide a more powerful relative position bias to facilitate the learning of the deformable attention, which will be discussed in the following sections.
+
+**Deformable attention module.** As illustrated in Figure **2**(a), given the input feature map $x \in \mathbb{R}^{H \times W \times C}$ , a uniform grid of points $p \in \mathbb{R}^{H_G \times W_G \times 2}$ are generated as the references. Specifically, the grid size is downsampled from the input feature map size by a factor r, $H_G = H/r$ , $W_G =$ W/r. The values of reference points are linearly spaced 2D coordinates $\{(0,0),\ldots,(H_G-1,W_G-1)\}$ , and then we normalize them to the range [-1, +1] according to the grid shape $H_G \times W_G$ , in which (-1, -1) indicates the top-left corner and (+1,+1) indicates the bottom-right corner. To obtain the offset for each reference point, the feature maps are projected linearly to the query tokens $q = xW_q$ , and then fed into a light weight sub-network $\theta_{\text{offset}}(\cdot)$ to generate the offsets $\Delta p = \theta_{\text{offset}}(q)$ . To stabilize the training process, we scale the amplitude of $\Delta p$ by some predefined factor s to prevent too large offset, *i.e.*, $\Delta p \leftarrow s \tanh{(\Delta p)}$ . Then the features are sampled at the locations of deformed points as keys and values, followed by projection matrices:
+
+$$q = xW_q, \ \tilde{k} = \tilde{x}W_k, \ \tilde{v} = \tilde{x}W_v, \tag{6}$$
+
+with
+$$\Delta p = \theta_{\text{offset}}(q), \ \tilde{x} = \phi(x; p + \Delta p).$$
+ (7)
+
+ $\tilde{k}$ and $\tilde{v}$ represent the deformed key and value embeddings respectively. Specifically, we set the sampling function $\phi(\cdot;\cdot)$ to a bilinear interpolation to make it differentiable:
+
+
+$$\phi\left(z;(p_{x},p_{y})\right) = \sum_{(r_{x},r_{y})} g(p_{x},r_{x})g(p_{y},r_{y})z[r_{y},r_{x},:], \quad (8)$$
+
+where $g(a,b) = \max(0,1-|a-b|)$ and $(r_x,r_y)$ indexes all the locations on $z \in \mathbb{R}^{H \times W \times C}$ . As g would be non-zero only on the 4 integral points closest to $(p_x,p_y)$ , it simplifies Eq.(8) to a weighted average on 4 locations. Similar to existing approaches, we perform multi-head attention on q,k,v and adopt relative position offsets R. The output of an attention head is formulated as:
+
+$$z^{(m)} = \sigma \left( q^{(m)} \tilde{k}^{(m)\top} / \sqrt{d} + \phi(\hat{B}; R) \right) \tilde{v}^{(m)}, \quad (9)$$
+
+where $\phi(\hat{B}; R) \in \mathbb{R}^{HW \times H_G W_G}$ correspond to the position embedding following previous work [26] while with several adaptations. Details will be explained later in this section. Features of each head are concatenated together and projected through $W_o$ to get the final output z as Eq.(3).
+
+**Offset generation.** As we have stated, a sub-network is adopted for offset generation which consumes the query
+
+features and outputs the offset values for reference points respectively. Considering that each reference point covers a local $s \times s$ region (s is the largest value for offset), the generation network should also have the perception of the local features to learn reasonable offsets. Therefore, we implement the sub-network as two convolution modules with a nonlinear activation, as depicted in Figure 2(b). The input features are first passed through a $5\times 5$ depthwise convolution to capture local features. Then, GELU activation and a $1\times 1$ convolution is adopted to get the 2D offsets. It is also worth noticing that the bias in $1\times 1$ convolution is dropped to alleviate the compulsive shift for all locations.
+
+**Offset groups.** To promote the diversity of the deformed points, we follow a similar paradigm in MHSA, and split the feature channel into G groups. Features from each group use the shared sub-network to generate the corresponding offsets respectively. In practice, the head number M for the attention module is set to be multiple times of the size of offset groups G, ensuring that multiple attention heads are assigned to one group of deformed keys and values.
+
+**Deformable relative position bias.** Relative position bias encodes the relative positions between every pair of query and key, which augments the vanilla attention with spatial information. Considering a feature map with shape $H\times W$ , its relative coordinate displacements lie in the range of [-H,H] and [-W,W] at two dimensions respectively. In Swin Transformer [26], a relative position bias table $\hat{B} \in \mathbb{R}^{(2H-1)\times(2W-1)}$ is constructed to obtain the relative position bias B by indexing the table with the relative displacements in two directions. Since our deformable attention has continuous positions of keys, we compute the relative displacements in the normalized range [-1,+1], and then interpolate $\phi(\hat{B};R)$ in the parameterized bias table $\hat{B} \in \mathbb{R}^{(2H-1)\times(2W-1)}$ by the continuous relative displacements in order to cover all possible offset values.
+
+Computational complexity. Deformable multi-head attention (DMHA) has a similar computation cost as the counterpart in PVT or Swin Transformer. The only additional overhead comes from the sub-network that is used to generate offsets. The complexity of the whole module can be summarized as:
+
+$$\Omega(\text{DMHA}) = \underbrace{2HWN_{\text{S}}C + 2HWC^2 + 2N_{\text{S}}C^2}_{\text{vanilla self-attention module}} + \underbrace{(k^2 + 2)N_{\text{S}}C}_{\text{offset network}},$$
+(10)
+
+where $N_s = H_G W_G = HW/r^2$ is the number of sampled points. It can be immediately seen that the computational cost of the offset network has linear complexity w.r.t. the channel size, which is comparably minor to the cost for attention computation. Typically, consider the third stage of a Swin-T [26] model for image classification where H = W = 14, $N_s = 49$ , C = 384, the computational cost for the attention module in a single block is 79.63M FLOPs. If equipped with our deformable module (with k = 5), the ad-
+
+
+
+Figure 3. An illustration of DAT architecture. $N_1$ to $N_4$ are the numbers of stacked successive local attention and shift-window / deformable attention blocks. k and k denote the kernel size and stride of the convolution layer in patch embeddings.
+
+ditional overhead is 5.08M Flops, which is only 6.0% of the whole module. Additionally, by choosing a large downsample factor r, the complexity will be further reduced, which makes it friendly to the tasks with much higher resolution inputs such as object detection and instance segmentation.
+
+We replace the vanilla MHSA with our deformable attention in the Transformer (Eq.(4)), and combine it with an MLP (Eq.(5)) to build a deformable vision transformer block. In terms of the network architecture, our model, Deformable Attention Transformer, shares a similar pyramid structure with [7, 26, 31, 36], which is broadly applicable to various vision tasks requiring multiscale feature maps. As illustrated in Figure 3, an input image with shape $H \times W \times 3$ is firstly embedded by a 4×4 non-overlapped convolution with stride 4, followed by a normalization layer to get the $\frac{H}{4} \times \frac{W}{4} \times C$ patch embeddings. Aiming to build a hierarchical feature pyramid, the backbone includes 4 stages with a progressively increasing stride. Between two consecutive stages, there is a non-overlapped 2×2 convolution with stride 2 to downsample the feature map to halve the spatial size and double the feature dimensions. In classification task, we first normalize the feature maps output from the last stage and then adopt a linear classifier with pooled features to predict the logits. In object detection, instance segmentation and semantic segmentation tasks, DAT plays the role of a backbone in an integrated vision model to extract multiscale features. We add a normalization layer to the features from each stage before feeding them into the following modules such as FPN [23] in object detection or decoders in semantic segmentation.
+
+We introduce successive local attention and deformable attention blocks in the third and the fourth stage of DAT. The feature maps are firstly processed by a window-based local attention to aggregate information locally, and then passed through the deformable attention block to model the global relations between the local augmented tokens. This alternate design of attention blocks with local and global receptive fields helps the model learn strong representations, sharing a similar pattern in GLiT [5], TNT [15] and Point-
+
+
+
+| DAT Architectures | | | | | | | |
+|--------------------------|--------------------|--------------------|---------------------|--|--|--|--|
+| | DAT-T | DAT-S | DAT-B | | | | |
+| Store 1 | $N_1 = 1, C = 96$ | $N_1 = 1, C = 96$ | $N_1 = 1, C = 128$ | | | | |
+| Stage 1 $(56 \times 56)$ | window size: 7 | window size: 7 | window size: 7 | | | | |
+| (30 × 30) | heads: 3 | heads: 3 | heads: 4 | | | | |
+| Store 2 | $N_2 = 1, C = 192$ | $N_2 = 1, C = 192$ | $N_2 = 1, C = 256$ | | | | |
+| Stage 2 $(28 \times 28)$ | window size: 7 | window size: 7 | window size: 7 | | | | |
+| (20 × 20) | heads: 6 | heads: 6 | heads: 8 | | | | |
+| | $N_3 = 3, C = 384$ | $N_3 = 9, C = 384$ | $N_3 = 9, C = 512$ | | | | |
+| Stage 3 | window size: 7 | window size: 7 | window size: 7 | | | | |
+| $(14 \times 14)$ | heads: 12 | heads: 12 | heads: 16 | | | | |
+| | groups: 3 | groups: 3 | groups: 4 | | | | |
+| | $N_4 = 1, C = 768$ | $N_4 = 1, C = 768$ | $N_4 = 1, C = 1024$ | | | | |
+| Stage 4 | window size: 7 | window size: 7 | window size: 7 | | | | |
+| $(7 \times 7)$ | heads: 24 | heads: 24 | heads: 32 | | | | |
+| | groups: 6 | groups: 6 | groups: 8 | | | | |
+
+Table 1. Model architecture specifications. $N_i$ : Number of block at stage i. C: Channel dimension. window size: Region size in local attention module. heads: Number of heads in DMHA. groups: Offset groups in DMHA.
+
+former [29]. Since the first two stages mainly learn local features, deformable attention in these early stages is less preferred. In addition, the keys and values in the first two stages have a rather large spatial size, which greatly increase the computational overhead in the dot products and bilinear interpolations in deformable attention. Therefore, to achieve a trade-off between model capacity and computational burden, we only place deformable attention in the third and the fourth stage and adopt the shift-window attention in Swin Transformer [26] to have a better representation in the early stages. We build three variants of DAT in different parameters and FLOPs for a fair comparison with other Vision Transformer models. We change the model size by stacking more blocks in the third stage and increasing the hidden dimensions. The detailed architectures are reported in Table 1. Note that there are other design choices for the first two stages of DAT, e.g., the SRA module in PVT. We show the comparison results in Table 7.
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
\ No newline at end of file
diff --git a/2202.12162/paper_text/intro_method.md b/2202.12162/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..82f0be29421906786926daf845920a7e090a3b44
--- /dev/null
+++ b/2202.12162/paper_text/intro_method.md
@@ -0,0 +1,31 @@
+# Introduction
+
+Are our artificial intelligence systems capable of reasoning? Or like *Clever Hans*, they use various cues only tangentially related to the task and rely on rote memorization with poor generalization? [@pfungst1911clever; @Johnson_2017_CVPR] This work revisits such a question and proposes an interactive framework with the communication channel between two players. The first player, which reasoning capabilities we are about to test, performs visual reasoning tasks, we call it . The second player, which we call the , is manipulating the scene so that it *fools* the first player even though those changes still lead to correct reasoning steps among humans. Both players interact with each other only through questions, answers and the visual scene as shown in Figure [1](#two_agents){reference-type="ref" reference="two_agents"}. If the manipulating the scene causes the to change its answer even though the new scene is still valid for the same question and answer, it is then the reasoning failure. It is similar to the following situation. Imagine a box is placed between two spheres. If you ask a question, *is there a box between two spheres?*, the answer should be positive. Now, if we move the box anywhere so it does not cross any of the spheres, and ask the same question, the response should remain unchanged. In other words, we postulate that reasoning outputs of agents need to be invariant under scene configurations that are consistent with the questions-answer pairs. Moreover, in the spirit of generic adversarial attacks, we seek configurations that also pose little if any reasoning challenges for humans.
+
+We propose an automatic and agnostic pipeline to benchmark the reasoning capabilities of various models, only assuming they can communicate by answering questions about the scene. Due to the recent stream of research in vision-and-language [@VinVL; @Defense; @GraphReasoningVQA; @VLBert; @vcrcnn; @kamath2021mdetr; @lxmert; @Uniter], we believe there will be an increasing number of vision models that operate through language. Moreover, we also consider the visual question answering framework set-up as a two-player system as an excellent benchmarking pipeline. We perform all tests by scene manipulations and observing how a tested model behaves under such changes. The pipeline does not require any knowledge of the internals of the tested model. It also does not manipulate the sensory information of such a model, e.g., pixels in the images, and all the manipulations are physically meaningful. Even though our current pipeline uses synthetic scenes as only those can easily be automatically manipulated, our results have also real-world ramifications. If models are susceptible to semantically *meaningless* changes[^1] in scene configurations, in a synthetic setting, there are valid concerns that real-world robots could also be prone to manipulation of objects in a room. Finally, our work also questions the possibility of training and benchmarking networks in a purely data-driven and offline, static manner.
+
+The main contributions of our work could be summarized in three points.
+
+***First***, we propose a strong *black-box* adversarial test, which makes no assumptions about the underlying mechanics of a tested model, formulated as a game between two players. Our test does not require any direct access to the tested model, even through its sensory information. In particular, it does not require gradients, output probabilities, or any access to the perceived image. Our work also deviates from bounded perturbations and instead focuses on global scene manipulations that are still consistent with the task constraints, and can change the behavior of a tested model.\
+***Second***, we reformulate visual reasoning by integrating visual question answering with zero-sum two-player game frameworks. Under our novel formulation, a visual and adversary agents compete against each other through content manipulation. We believe that this is an initial step towards more sophisticated frameworks that integrate computer vision with multi-agent systems.\
+***Third***, we explore the limits of the data-driven approaches in synthetic visual scenarios, and demonstrate that current CLEVR models are lacking the efficiency to learn robust reasoning steps.
+
+# Method
+
+In this section, we explain briefly how the CLEVR dataset [@Johnson_2017_CVPR] is constructed and introduce our notation and definitions. is a synthetic visual question answering dataset introduced by @Johnson_2017_CVPR, which consists of about 700k training and 150k validation image-question-answer triplets. Images are artificially constructed and rendered from scene graphs -- a special structure containing information about object attributes such as position or color. Such a scene graph is also used to synthesize the ground-truth question-answer pairs by expanding templates according to the depth-first-search ordering. Ambiguous scenes are rejected. Each image represents an isometric view of the scene containing from two to ten objects. There are three classes of objects, *spheres*, *cubes* and *cylinders*. Each object can also be either large or small and has one color out of four (brown, purple, cyan, yellow). It can also be either metallic or rubber-made. Every object has $x$ and $y$ coordinates that are confined within the $(-3, +3)$ range. We use the same generation process to render modified scenes. Various models have been introduced to work with the CLEVR dataset, some even 'solving' the dataset by achieving near perfect performance. Despite the strong offline performance, we test if those models' performance perpetuates in the more interactive setting where configurations of the scene could be changed. Whenever possible, we use pre-trained CLEVR models. Otherwise, we train the remaining models from scratch by making sure we achieve results similar to published accuracy numbers on the validation set. We summarize all the models in Table [\[model_table\]](#model_table){reference-type="ref" reference="model_table"}. We show the accuracy on the CLEVR dataset (*Accuracy*), indicate if an architecture is trained from scratch (*Re-trained*), briefly describe how multi-modal fusion and reasoning is conducted (*Reasoning Mechanism*), and indicate any extra privileged information required during the training process (*Extra*). For instance, some models require extra access to functional programs used during the dataset generation, use scene graphs as a supervisory signal (states), or always operate on scene graphs (input-states). Otherwise, the models were trained only from image-question-answer triples. We formulate our problem as a between two players, and . The takes as input question-image pairs and provide answers to such questions. Some models use states (scene-graphs) that replace images or require programs [@Johnson_2017_CVPR]. The whole game consists of all CLEVR data points. For our purpose, we extend the notion of the into . The rules of are identical to the whole . The only difference is that each operates on a subset of the CLEVR dataset. We define the size of a by the number of datapoints that are attached to that . We sample data points for each randomly and mutually exclusively. have analogies in the adversarial perturbations literature. of size one resemble per-image adversarial perturbations [@FGSM; @Deepfool] whereas a that has all data points is similar to universal adversarial perturbations [@moosavi2017universal]. In this work, we investigate various sizes but due to the sheer scale we were unable to use the whole game as the . Larger make the optimization process more difficult as the domain where the needs to operate increases. The training is also much more time-consuming and a sequential process. Instead, we can train multiple players on different independently and thus massively. We leave the arduous training of the universal on the whole as a possible future direction.
+
+::: center
+:::
+
+We need to ensure that create valid scenes that are *consistent* and *in-distribution*. Both properties are guaranteed by our environment enforcers. Since scene manipulation may change the answer for a given question, we need to ensure this does not happen. That is, the new scene is still *consistent* with the question-answer pair. The question-relevance enforcer achieves that by running functional programs associated with each question [@Johnson_2017_CVPR] on the modified scene-graph. Hence, it gets the new ground-truth answer. The enforcer rejects the new scene if that new answer differs from the previous one. In this way, it guarantees the newly generated scenes give the same answers as the original scenes on the same question. Thus, we can generate equivalent scenes containing the same objects that have identical answers for the same questions. Using that enforcer, we can test if the 's answers are invariant under such an equivalent class of scenes. Even with the question-relevance enforcer, the may still produce undesired outputs. For instance, it can stretch the whole scene thus violating the scene boundaries from the original CLEVR dataset, making, e.g., everything to look very small ( in the appendix). Although it is still an interesting form of adversarial scene manipulation, we focus rather on the *in-distribution* scene manipulations that respect the original boundaries. To enforce that property, we use a scene-constraint enforcer that checks the boundaries of the scene. Without that enforcer, the would quickly resort to stretching the whole scenes, achieving a form of adversarial attack that uses distribution shifts rather than content manipulation. It does so, e.g., by moving the camera away until objects are barely visible. We give a few such examples in the appendix ().
+
+
+
+Three pairs of synchronized exo-ego (v3, e4) frames from Assembly101.
+
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+# Introduction
+
+The remarkable progress in self-supervised learning has revolutionized the fields of molecule property prediction and molecule generation through the learning of expressive representations from large-scale unlabeled datasets [@hu2019strategies; @liu2019n; @hu2020gpt; @sun2019infograph; @liu2022molecular; @zaidi2022pre; @liu2021pre; @liu2023group; @liu2023symmetry]. Unsupervised representation learning can generally be categorized into two types [@liu2021graph; @xie2021self; @wu2021self; @liu2021self; @liu2021pre]: generative-based methods, encouraging the model to encode information for recovering the data distribution; and contrastive-based methods, encouraging the model to learn invariant features from multiple views of the same data. However, despite the success of large language models like GPT-3 [@madotto2020language; @zhao2023survey] trained using an autoregressive generation approach , learning robust representations from molecular data remains a significant challenge due to their complex graph structures. Compared to natural language and computer vision data [@vahdat2021score; @ramesh2022hierarchical], molecular representations exhibit more complex graph structures and symmetries [@thomas2018tensor]. As molecular dynamics follows the principle of non-equilibrium statistical mechanics [@wood1976molecular], diffusion models, as a generative method inspired by non-equilibrium statistical mechanics [@sohl2015deep; @dockhorn2021score], are a natural fit for 3D conformation generation. Previous researches have demonstrated the effectiveness of diffusion models, specifically 2D molecular graphs [@jo2022score; @vignac2022digress] or 3D molecular conformers [@du2022se; @liugroup; @shi2021learning; @hoogeboom2022equivariant], for molecular structure generation tasks. However, a crucial question remains: *Can diffusion models be effectively utilized for jointly learning 2D and 3D latent molecular representations?*
+
+
+
+Pipeline of MoleculeJAE. For each individual molecule, MoleculeJAE utilizes the reconstructive task to perform denoising. For pairwise molecules, it conducts a contrastive learning paradigm to fit the trajectory.
+
+
+Answering this question is challenging, given that diffusion models are the first generative models based on trajectory fitting. Specifically, diffusion models generate a family of forward random trajectories by either solving stochastic differential equations [@song2020score; @du2022flexible] or discrete Markov chains [@austin2021structured; @li2022diffusion; @vignac2022digress], and the generative model is obtained by learning to reverse the random trajectories [@ho2020denoising; @bansal2022cold]. It remains unclear how the informative representation manifests itself in diffusion models, as compared to other generative models that explicitly contain a semantically meaningful latent representation, such as GAN [@goodfellow2020generative] and VAE [@kingma2013auto]. In addition to the generative power of diffusion models, we aim to demonstrate the deep relationship between the forward process of diffusion models and data augmentation [@tian2020makes; @sinha2021negative], a crucial factor in contrastive learning. As a result, we ask whether a trajectory-based self-supervised learning paradigm that leverages both the generative and contrastive benefits of diffusion models can be used to obtain a powerful molecular representation.
+
+**Our approach.** This work presents MoleculeJAE (Molecule Joint Auto-Encoding), a novel trajectory learning framework for molecular representation that captures both 2D (chemical bond structure) and 3D (conformer structure) information of molecules. Our proposed method is designed to respect the $SE(3)$ symmetry of molecule data and is trained by fitting the joint distribution of the data's augmented trajectories extracted from the forward process of the diffusion model. By training the representation in this manner, our framework not only captures the information of real data distribution but also accounts for the corelation between the real data and its noised counterparts. Under certain approximations, this trajectory distribution modeling decomposes into a marginal distribution estimation and a trajectory contrastive regularization task. This multi-task approach yields an effective and flexible framework that can simultaneously handle various types of molecular data, ranging from SE(3)-equivariant 3D conformers to discrete 2D bond connections. Furthermore, in contrast to diffusion models used for generation tasks that only accept noise as input, most downstream tasks have access to ground-truth molecular structures. To leverage this advantage better, we incorporate an equivariant graph neural network (GNN) block into the architecture, inspired by [@nichol2021improved; @preechakul2022diffusion], to efficiently encode crucial information from the ground-truth data. Analogous to conditional diffusion models [@nichol2021improved; @zhang2023adding], the encoder's output serves as a meaningful guidence to help the diffused data accurately fitting the trajectory. In summary, our self-supervised learning framework unifies both contrastive and generative learning approaches from a trajectory perspective, providing a versatile and powerful molecular representation that can be applied to various downstream applications.
+
+Regarding experiments, we evaluate MoleculeJAE on 20 well-established tasks drawn from the geometric pretraining literature [@zaidi2022pre; @liu2021pre], including the energy prediction at stable conformation and force prediction along the molecule dynamics. Our empirical results support that MoleculeJAE can outperform 12 competitive baselines on 15 tasks. We further conduct ablation studies to verify the effectiveness of key modules in MoleculeJAE.
+
+# Method
+
+Following Eq. [\[marginal\]](#marginal){reference-type="ref" reference="marginal"}, we consider the 'marginal' distribution $$q_{\theta}(x_0): = \int p_{\theta}(x_0,x_t)dx_t$$ by marginalizing out $x_t$. We define the corresponding energy function with respect to $x_0$ as $$\Bar{\textbf{E}}_{\theta} (x_0) = - \log \int \exp (-\textbf{E}_{\theta}(x_0, x_t)) d x_t .$$
+
+From the definition of $\Bar{\textbf{E}}_{\theta} (x_0)$, the distribution $q_{\theta}(x_0)$ shares the same normalization constant with $q_{\theta}(x_0,x_t)$. Therefore we have $$\frac{\partial \log f_{\theta}(x_0,x_t)}{\partial \theta} = - \frac{\partial \Bar{\textbf{E}}_{\theta}(x_0, x_t)}{\partial \theta} + \frac{\partial \Bar{\textbf{E}}_{\theta} (x_0)}{\partial \theta},$$ where $f_{\theta}(x_0 , x_t)$ denotes the normalized conditional probability $q_{\theta}(x_t | x_0)$. By the conditional probability formula, it is obvious that: it's obvious that $$\begin{equation}
+\frac{\partial \log q_{\theta}(x_0, x_t)}{\partial \theta} = \frac{\partial \log f_{\theta}(x_0 , x_t)}{\partial \theta} + \frac{\partial \log q_{\theta}(x_0)}{\partial \theta}.
+\end{equation}$$ By taking the empirical expectation with respect to the sampled pair $(x_0,x_t) \sim p(x_0,x_t)$, we find that the gradient of the Maximum Likelihood allows the following decomposition: $$\begin{equation}
+ \label{decomposition} \small
+\Tilde{\mathbb{E}}_{p(x_0,x_t)}\left[\frac{\partial \log p_{\theta}(x_0, x_t)}{\partial \theta} \right]= \underbrace{\Tilde{\mathbb{E}}_{p(x_0)}\left[\frac{\partial \log q_{\theta}(x_0)}{\partial \theta}\right]}_{\textbf{Gradient of reconstruction}} + \underbrace{\Tilde{\mathbb{E}}_{p(x_0,x_t)}\left[\frac{\partial \log f_{\theta}(x_0 , x_t)}{\partial \theta}\right]}_{\textbf{Gradient of contrastive learning}} .
+\end{equation}$$ Here, we use $\Tilde{\mathbb{E}}$ to denote the expectation with respect to the empirical expectation. In this way, we find that maximizing the likelihood $p_{\theta}(x_0,x_t)$ is equivalent to solving: $$\text{argmax}_{\theta} \left\{\Tilde{\mathbb{E}}_{p(x_0)}\left[\frac{\partial \log q_{\theta}(x_0)}{\partial \theta}\right] + \Tilde{\mathbb{E}}_{p(x_0,x_t)}\left[\frac{\partial \log f_{\theta}(x_0 , x_t)}{\partial \theta}\right] \right\}.$$
+
+
+
+Modular Design of MoleculeJAE.
+
+
+In this section, we provide the details for the model design described in Section [\[sec:model_architecture\]](#sec:model_architecture){reference-type="ref" reference="sec:model_architecture"}. Specifically, we illustrate how to obtain the latent representation $h_{\theta}$ from $x_0 = (P_0,H_0,E_0)$ and $x_t = (P_t,H_t,E_t)$ (the solution of Eq. [\[joint sde\]](#joint sde){reference-type="ref" reference="joint sde"} at time $t$). It is important to note that in standard diffusion generative models [@ho2020denoising; @song2020score], only $x_t$ is fed into the score neural network.
+
+The 3D part $(H_0, E_0)$ and $(H_t,E_t)$are transformed by two equivariant 3D GNNs, and we denote the invariant node-wise output representation by $f_0$ and $f_t$. Additionally, we embed the time $t$ (which is essential for trajectory learning) using Fourier embedding, following [@song2020score]: $$\text{Emd}(t) : = \textbf{Fourier}(t).$$ We obtain the 3D node-wise representation by concatenating the Fourier embedding, $f_0$, and $f_t$: $$\textbf{Node}_{3D} = \textbf{MLP}\ [\text{Emd}(t) \mathbin\Vert f_0 \mathbin\Vert f_t ].$$ Up to this point, we haven't utilized the 2D information $(E_0, E_t)$. Unlike the node-wise 3D representation, we encode $(E_0, E_t)$ as a weighted adjacency matrix $W$: $$W = \textbf{MLP}\ [\text{Emd}(t) \mathbin\Vert E_0 \mathbin\Vert E_t ].$$ Combing $\textbf{Node}_{3D}$ and the adjacency matrix $W$, we implement a dense graph convolution neural network (GCN) to obtain the final (node-wise) latent representation $h_{\theta}$, following [@jo2022score]: $$h_{\theta} = \textbf{GCN}\ (\text{Node}_{3D}, W).$$
+
+To obtain the $SE(3)$ equivariant and reflection anti-equivariant vector field [@jing2022torsional] $\text{Score}(P_t)$ from $h_{\theta}$, we implement the node-wise equivariant frame and perform tensorization, following [@du2022se; @du2023new]. Consider node $i$ with 3D position $\textbf{x}_i$, let $\Bar{\textbf{x}}_i : = \frac{1}{N}\sum_{\textbf{x}_j \in \mathcal{N}(\textbf{x}_i)} \textbf{x}_j$ be the center of mass around $\textbf{x}_i$'s neighborhood. Then the orthonormal equivariant frame $\mathcal{F}_i : = (\textbf{e}^i_1,\textbf{e}^i_2,\textbf{e}^i_3)$ with respect to $\textbf{x}_i$ is defined as:
+
+$$\begin{equation}
+\label{eq:node frame}
+(\frac{\textbf{x}_i - \Bar{\textbf{x}}_i}{\left\lVert\textbf{x}_i - \Bar{\textbf{x}}_i\right\rVert}, \frac{\Bar{\textbf{x}}_i \times \textbf{x}_i}{\left\lVert\Bar{\textbf{x}}_i \times \textbf{x}_i\right\rVert},\frac{\textbf{x}_i - \Bar{\textbf{x}}_i}{\left\lVert\textbf{x}_i - \Bar{\textbf{x}}_i\right\rVert} \times \frac{\Bar{\textbf{x}}_i \times \textbf{x}_i}{\left\lVert\Bar{\textbf{x}}_i \times \textbf{x}_i\right\rVert} ),
+\end{equation}$$ where $\times$ denotes the cross product, which is $SE(3)$ equivariant and reflection anti-equivariant. Then, we transform $h_{\theta} \in \mathbf{R}^{N \times L}$ to $h_{3D} \in \mathbf{R}^{N \times 3}$: $$h_{3D} = (h_1,h_2,h_3) := \textbf{MLP}\ (h_{\theta}).$$ Finally, for node $i$, $$\text{Score}^i(P_t) = h_1 \cdot \textbf{e}^i_1 + h_2 \cdot \textbf{e}^i_2 + h_3 \cdot \textbf{e}^i_3.$$
+
+$\text{Score}(E_t)$ represents the probability gradient with respect to the molecule bonds. Therefore, $\text{Score}(E_t)$ has the same shape as the dense adjacency matrix and is $SE(3)$ invariant. To obtain $\text{Score}(E_t)$, we leverage graph multi-head attention [@baek2021accurate] to obtain a dense attention matrix $A_t$ (see [@baek2021accurate] for the explicit formula): $$A_t = \textbf{Att}\ (h_{\theta}).$$ Then, we apply an **MLP** to the edge-wise $A_t$ to obtain the final 2D score function: $$\text{Score}(E_t) = \textbf{MLP}\ (A_t).$$ See Figure [2](#fig:network){reference-type="ref" reference="fig:network"} for a graphical representation.
+
+:::: adjustbox
+max width=
+
+::: {#tab:sec:MD17_statistics}
+ Pretraining Aspirin $\downarrow$ Benzene $\downarrow$ Ethanol $\downarrow$ Malonaldehyde $\downarrow$ Naphthalene $\downarrow$ Salicylic $\downarrow$ Toluene $\downarrow$ Uracil $\downarrow$
+ ------------- ---------------------- ---------------------- ---------------------- ---------------------------- -------------------------- ------------------------ ---------------------- ---------------------
+ Train 1K 1K 1K 1K 1K 1K 1K 1K
+ Validation 1K 1K 1K 1K 1K 1K 1K 1K
+ Test 209,762 47,863 553,092 991,237 324,250 318,231 440,790 131,770
+
+ : Some basic statistics on MD17.
+:::
+::::
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+# Introduction
+
+The success of many deep learning methods is often credited to the availability of large amounts of labeled data [@imagenet; @celeb-500k; @hypersim-data; @vggsound-data]. However, labeling necessitates manual annotation, which is a time-consuming and labor-intensive process. Distant supervision or weak supervision methods address this issue by using an external knowledge base [@distant_supervision_KB], pre-trained models [@bach2019snorkel], and heuristics approaches [@distant_supervision_heuristic] to generate inexpensive but potentially noisy class labels for downstream tasks. These methods use an alternative source of information to assign labels to the training data and thus avoid the need for extensive manual annotation. However, the pseudo labels generated by such methods are often noisy in nature and can have a detrimental impact on downstream performance.
+
+Programmatic weak supervision [@PWS_survey; @PWS_dehghani; @PWS_lang; @PWS_ratner2016; @PWS_ratner2020snorkel; @PWS_riedel] addresses this issue by combining the prediction of multiple such noisy sources of labels often known as label functions (LFs) systematically. In PWS, a label model is trained to combine the prediction of these label functions to estimate the unobserved ground truth label for a given training sample. The generated pseudo label is then used for downstream training. For a given set of label functions, the quality of the pseudo label is often determined by the performance of the label model. Various approaches have been suggested to effectively train the label model [@PWS_survey] without knowing the underlying ground truth class labels. While approaches like [@PWS_ratner2016; @PWS_ratner2020snorkel; @PWS_ratner2019_metal; @PWS_fu_fastsquid] use only the prediction of label functions to estimate the pseudo label, encoder-based label models also consider data features to estimate pseudo labels [@cauchy_end_to_end; @gen_model_PWS_vice_versa].
+
+Recently, @gen_model_PWS_vice_versa proposed WSGAN, a novel training strategy to efficiently generate pseudo labels for a given image dataset. Their approach combines InfoGAN with an encoder-based label model [@cauchy_end_to_end]. The key idea is to train both InfoGAN [@infogan] and label model simultaneously and leverage the disentangled latent factors learned by InfoGAN to align the label model's predictions. This training process offers several advantages. The synchronized training of the label model and InfoGAN allows the efficient utilization of the disentangled latent factors of InfoGAN for the training of the label model. Simultaneously, the pseudo labels generated by the label model facilitate the generation of class-conditioned images, which can benefit downstream tasks as an additional form of data augmentation.
+
+Nevertheless, the implementation of such a training process presents several challenges. The disentangled latent representation of InfoGAN is learned by maximizing the mutual information between the discrete conditional input and the generated images. However, the representations (latent factors) learned from InfoGAN may not be class-specific. In such a scenario, aligning the prediction of the label model with such latent factors can hurt the accuracy of the label model. Furthermore, the predictions of the label models are often noisy in nature, and using the pseudo labels directly for GAN training can make the overall training process unstable, impacting the quality of images generated by InfoGAN.
+
+In this study, we address these challenges by training a noise-aware classifier-guided conditional GAN [@ACGAN] for the training of label model and synthetic image generation. In contrast to conventional conditional GANs that rely on authentic ground truth labels during training, our approach operates within a weak supervision framework, where the pseudo labels generated by the label model are used to train the classifier. To handle the noise associated with the pseudo labels, the classifier is trained using a noise-aware symmetric cross entropy loss [@symmetric_cross_entropy] in an adaptive fashion [@psuedolabel_gan_Morerio]. Within the current setup, as training proceeds, a new set of refined dataset and associated pseudo labels generated by the label model is used to train the classifier.
+
+Further, a recent line of research by @subset_ws_neurips22 shows that, in weak supervision, a subset of the most representative training samples yields superior performance compared to using the entire dataset for downstream training of a classifier. Motivated by this, we propose to utilize a subset of highly representative and diverse examples that exhibit minimal uncertainty with the pseudo labels. We accomplish this by incorporating a data subset selection process for weak supervision by formulating it as a submodular maximization problem [@submodularity_AI_blimes; @krause_submodular_maximization] with a knapsack constraint defined over the overall entropy of the pseudo labels of the selected samples. [@krause_submodular_maximization; @submodular_knapsack_NAS]. Our contributions are summarized below:
+
+\(1\) We propose a novel technique to fuse a classifier-guided conditional GAN with an encoder-based label model. Within the framework of programmatic weak supervision (PWS), this helps in efficient training of the label model and generation of class conditional images, (2) We present a novel approach for subset selection to be used in tandem with PWS by identifying the most diverse and representative samples via a submodular maximization technique, aiding in the reduction of the uncertainty associated with the training dataset, and (3) We investigate the impact on the overall performance of the label model as well as on the quality of images using the proposed subset selection scheme and compare our method with the current state-of-the-art.
+
+# Method
+
+Let there be $n$ training data samples $\Tilde{{\mathcal{D}}} = \{x_1, x_2, \hdots x_n\}$ drawn i.i.d from a distribution ${\mathcal{P}}_{x}$. The objective of our formulation is two folds: firstly, we want to infer the class labels for these samples i.e., $y \in \{0 \hdots {\mathcal{C}}\}$ and secondly, we want to sample synthetic images whose marginal is represented as ${\mathcal{P}}_{x}$. In our setting, instead of observing true labels, we have access to the predictions of ${\mathcal{K}}$ label functions on given training data. Each label function $\lambda_{k} (x_i)$ (where $k \in \{1 \hdots{\mathcal{K}}\}$) generates a noisy prediction of the true class label for a sample $x_{i}$. These label functions can either predict among a given set of possible class labels i.e, $\lambda_{k} (x_i) \in \{0 \hdots {\mathcal{C}}\}$ or can abstain from making a prediction because of low confidence [@PWS_ratner2016]. Let us refer to the data with a prediction from at least one label function as $\Tilde{{\mathcal{D}}_t} \in \Tilde{{\mathcal{D}}}$ (non-abstained dataset). For each data sample we have an associated prediction from ${\mathcal{K}}$ label functions represented as $\Lambda(x_i) = (\lambda_1(x_i), \lambda_2(x_i) \hdots \lambda_{{\mathcal{K}}}(x_i))$. A label model (${\mathcal{L}}$) utilizes the predictions made by these LFs to predict a final pseudo label ($\hat{y_i}$) for a given input sample $x_i$. To train our noise-aware classifier, we generate a subset of original training data $\Tilde{{\mathcal{D}}_0} \in \Tilde{{\mathcal{D}}_t}$ and their associated pseudo labels ($\hat{{\mathcal{Y}}} = \{\hat{y_1} \hdots \hat{y}_{|\Tilde{{\mathcal{D}}}_0|}$ }) and study the impact of training the noise-aware classifier with the given subset on the overall performance of the conditional GAN and the label model.
+
+
+
+Overview of the proposed fusion between conditional GAN and Programmatic Weak Supervision. In the current setup, a subset of the non-abstained samples, selected by submodular maximization under a knapsack constraint, is used to train a noise-aware classifier. This classifier shares its network weights with the discriminator and label model. The noise-aware classifier is then employed to train a conditional GAN and refine the label model. As the training progresses and the label model becomes more accurate, a better subset is utilized to train the classifier.
+
+
+As stated previously, we have used a classifier to facilitate the training of the conditional GAN and the alignment of the label model. Given the absence of ground truth labels, the classifier is trained using the pseudo labels generated by the label model.
+
+The cross-entropy loss is a common choice of the loss function used to train a classifier. However, recent studies [@symmetric_cross_entropy; @feng2021can] have shown that a classifier trained only with cross-entropy loss under a noisy setting tends to exhibit prediction bias and is susceptible to overfitting. In the context of weak supervision, such a classifier can not only impact the performance of the conditional GAN but can also lead to further deterioration of the quality of pseudo labels generated by the label model.
+
+In our study, we address this issue by training our classifier using a symmetric cross-entropy loss (${\bm{l}}_{sce}$) [@symmetric_cross_entropy] between the pseudo label ($\hat{y} \sim \hat{{\mathcal{Y}}}$) generated by the label model, and the class conditional distribution modeled by the classifier (${\mathcal{P}}(t|x)$). The classifier is trained on a subset of the training dataset ($\Tilde{{\mathcal{D}}}_0$) selected by our proposed submodular maximization scheme. Symmetric cross-entropy loss is a weighted combination of standard cross-entropy loss (${\bm{l}}_{ce}$) and reverse cross-entropy loss (${\bm{l}}_{rce}$). While the cross-entropy loss provides a better convergence to the classifier, reverse cross-entropy loss provides robustness to different types of noise such as uniform and class conditional noise [@symmetric_cross_entropy; @robust_classifier_ghosh], thereby ensuring that the classifier does not overfit to noisy class labels. Mathematically, the symmetric cross-entropy loss (${\bm{l}}_{sce}$) is described in Eq. [\[eqn:classifier\]](#eqn:classifier){reference-type="ref" reference="eqn:classifier"}.
+
+$$\begin{align}
+{\bm{l}}_{ce}(x, \hat{y}) & = -\sum_{t=0}^{{\mathcal{C}}} \hat{y}_t \log({\mathcal{P}}(t|x)) \nonumber\\
+{\bm{l}}_{rce} (x, \hat{y}) & = -\sum_{t=0}^{{\mathcal{C}}} {\mathcal{P}}(t|x)\log(\hat{y}_t) \nonumber\\
+{\bm{l}}_{sce}(x, \hat{y}) & = \alpha {\bm{l}}_{ce}(x, \hat{y}) + \beta {\bm{l}}_{rce} (x, \hat{y}) \nonumber
+\\
+{\bm{l}}_{sce}^{\Tilde{{\mathcal{D}}_0}} & = \frac{1}{|\Tilde{{\mathcal{D}}_0}|} \sum_{x\sim \Tilde{{\mathcal{D}}_0}, \hat{y} \sim \hat{{\mathcal{Y}}}} {\bm{l}}_{sce}(x, \hat{y})
+ % \end{autobreak}
+ \label{eqn:classifier}
+\end{align}$$
+
+The classifier is trained in an adaptive manner [@psuedolabel_gan_Morerio], where a new set of data ($\Tilde{D}_0$) is introduced for training after a specific number of epochs. During the training, the given examples and their associated pseudo labels remain unchanged until the next iteration. This ensures that as the accuracy and performance of the label model improve, a more refined dataset and its corresponding pseudo labels are utilized for training. Simultaneously, it prevents the model from diverging due to frequent alterations in data samples and their associated pseudo labels.
+
+In our study, we have employed a classifier-guided generative adversarial network (GAN) to generate class conditional images [@ACGAN]. The generator (${\mathcal{G}}$) is fed a noise sample $z\sim N(0,\textit{I})$ and a one hot representation of class label $c\sim {\mathcal{P}}({\mathcal{C}})$ to sample an image ($x_g$ = ${\mathcal{G}}(z,c)$) from the parameterized distribution (${\mathcal{P}}_\theta$). The discriminator ($\mathcal{D}$) tries to distinguish between the images sampled from real data distribution (${\mathcal{P}}_x$) and the images sampled from parameterized distribution (${\mathcal{P}}_{\theta}$). For conditional image generation, the generator further maximizes the likelihood of the image ($x_g$) belonging to class c with respect to our noise-aware classifier. The objective function that is optimized for Conditional GAN training is as follows:
+
+$$\begin{align}
+ {\bm{l}}({\mathcal{G}}, {\mathcal{D}}) =& \underset{x\sim {\mathcal{P}}_x}{\mathbb{E}}\left[\log ({\mathcal{D}}(x))\right] + \underset{x\sim {\mathcal{P}}_\theta}{\mathbb{E}}\left[\log(1-{\mathcal{D}}(x))\right] \nonumber \\
+ \min_{{\mathcal{G}}} \max_{{\mathcal{D}}} {\bm{l}}_{gan} =& {\bm{l}}({\mathcal{G}}, {\mathcal{D}}) + \underset{x\sim {\mathcal{G}}(z,c), c \sim {\mathcal{P}}({\mathcal{C}})}{\mathbb{E}}\left[{\bm{l}}_{ce} (x,c)\right]
+ % \min{\gG}\max_{\gD} \v_{gan} = \gL_{gan} - \lambda \left[\underset{\hat{\gY} \sim \gL(\Lambda(x)), x\sim \gP_x}{\E}\left[ \gL_{\textit{sce}}(\hat{\gY}, \gC(x))\right]\right] \\
+ % \min_{\gG} V(\gG,\gD) + \lambda\left[\underset{c \sim \gP(c), x\sim \gP_\theta}{\E}\left[\gL_{\textit{ce}} (c, \gC(x))\right]\right]
+ % \\
+ % \underset{c \sim \gP(c), x\sim \gP_\theta}{\E}\left[\gL_{\textit{ce}}\right] = \frac{1}{\gB}\sum_{x}\sum_{t=0}^{\gM} c_{t}log(\gC(t|x))
+\end{align}$$
+
+In programmatic weak supervision, the label model accumulates the predictions made by different label functions to make an informed final estimation of the pseudo label associated with the given training sample. Generally, this is done by defining an accuracy potential function ($\phi_k$) to capture the association between the prediction made by a given label function and any class label $y$ ($\phi_k(\lambda_k,y)$). Similar to previous works [@gen_model_PWS_vice_versa; @PWS_ratner2020snorkel], we have defined the accuracy potential as an indicator function, $\phi_k(\lambda_k,y) = {\bm{1}}(\lambda_k = y)$ which captures the notion whether a given label function ($\lambda_k$) has predicted class $y$ for a given input sample. With each label function, there is an associated accuracy parameter (${\mathcal{A}}_k: {\mathcal{R}}^N\rightarrow (0,1)$), which determines the accuracy of its prediction. We have used sample-dependent accuracy parameters, where the accuracy of every such association is different for different input samples [@cauchy_end_to_end; @gen_model_PWS_vice_versa]. The label model (${\mathcal{L}}({\Lambda(x)})_j$), defined below, captures the probability associated with class $j$ for any input sample $x$. $$\begin{align}
+ {\mathcal{L}}({\Lambda(x)})_j = \frac{\exp(\sum_{i=0}^{{\mathcal{K}}}{\mathcal{A}}(x)_i \phi_i(\lambda_i(x), j))}{\sum_{{\bm{s}}\in y} \exp(\sum_{i=0}^{{\mathcal{K}}} {\mathcal{A}}(x)_i \phi_i(\lambda_i(x), {\bm{s}})) } \nonumber \\
+ \text{ where } j\in \{ 0 \hdots {\mathcal{C}}\}
+\end{align}$$
+
+The final pseudo label, associated with the input sample $x$, is defined as the class with the maximum probability.
+
+$$\begin{align}
+\hat{y} = \mathop{\mathrm{arg\,max}}_j {\mathcal{L}}({\Lambda(x)})_j
+\end{align}$$
+
+To enhance the precision of the generated pseudo labels, the label model is trained using the probabilities generated by the classifier for the samples from the non-abstained dataset ($\Tilde{{\mathcal{D}}}_t$). In this approach, we employ a linear layer followed by a softmax ($\hat{{\mathcal{F}}}$) for the alignment of probabilities between the output of the label model and the classifier (${\mathcal{P}}(t|x)$) [@gen_model_PWS_vice_versa], giving rise to the following loss function. $$\begin{align}
+ \centering
+ {\bm{l}}_{align}(x) = -\sum_{t=0}^{{\mathcal{C}}} {\mathcal{P}}(t|x)\log(\hat{{\mathcal{F}}}( {\mathcal{L}}(\Lambda(x))_t)
+\end{align}$$ Since our classifier is trained iteratively by utilizing the pseudo labels generated through the label model $\big(\hat{y}$ in Eq. [\[eqn:classifier\]](#eqn:classifier){reference-type="ref" reference="eqn:classifier"}$\big)$, we ensure that the pseudo labels are not entirely random, especially during initial epochs. To accomplish this, we employ a new loss ${\bm{l}}_{decay}$ that assigns equal weights to all the accuracy parameters and gradually decay this loss as the label model becomes more accurate [@gen_model_PWS_vice_versa]. Under this loss, the accuracy parameter for all the label functions is approximated to a value of $0.5$, and the overall loss is decayed by a rate $\mu$ as the training proceeds. Mathematically, it is equivalent to generating an accuracy parameter of 0.5 for all the label functions, which is approximated by a vector $0.5 *\overrightarrow{{\bm{1}}}$ where $\overrightarrow{{\bm{1}}}$ is a vector of ones with dimension equal to the number of label functions, giving rise to the following loss function : $$\begin{align}
+ \centering
+ {\bm{l}}_{decay}(x) = & \mu||{\mathcal{A}}(x)- 0.5 *\overrightarrow{{\bm{1}}}||^2
+\end{align}$$
+
+This process ensures that during the initial epochs, the label model approximates a majority vote, and as training progresses, it becomes more data-specific. Finally, the label model is trained using ${\bm{l}}_{lm}$, by combining alignment and decay loss ${\bm{l}}_{lm} = {\bm{l}}_{align} + {\bm{l}}_{decay}$.
+
+To facilitate better training of our noise-aware classifier, we select a subset of data ($\Tilde{D}_0$) from the training examples that have received at least one vote from the label functions ($\Tilde{D}_t$). We formulate this as a submodular optimization problem as in [@submodularity_AI_blimes; @mirzasoleiman2013distributed].
+
+::: {#Defn:submod .definition}
+**Definition 1**. *A function ${\mathcal{F}}: 2^{{\mathcal{V}}} \rightarrow R$ is said to be submodular for a finite set ${\mathcal{V}}$, if for every $A \subseteq B \subseteq {\mathcal{V}}$ and $\forall$ $v \in {\mathcal{V}}\setminus B$ ${\mathcal{F}}\big(A \bigcup v\big) - {\mathcal{F}}\big(A\big) \geq {\mathcal{F}}\big(B \bigcup v\big) - {\mathcal{F}}\big(B\big)$*
+:::
+
+Further, a specific class of submodular functions is called monotone if $\forall \big(A, B \subset {\mathcal{V}}\big)$ s.t. $A \subseteq B,$ ${\mathcal{F}}\big(A\big) \leq {\mathcal{F}}\big(B\big)$.
+
+A monotonous submodular function can be used to determine the utility of a given set of data for a downstream task, and an efficient subset of the data can be selected by maximizing the given function. For a cardinality constraint, the process begins with an empty set (${\mathcal{S}}_0$), and the selection proceeds iteratively in a greedy manner, selecting the data point that maximizes the submodular function until the given budget on the size is exhausted. Mathematically, if $v$ denotes the datapoint to be selected, then $v = \mathop{\mathrm{arg\,max}}_{ v \in {\mathcal{V}}\setminus {\mathcal{S}}_{n-1} } {\mathcal{F}}\big(v \bigcup {\mathcal{S}}_{n-1}\big) - {\mathcal{F}}\big({\mathcal{S}}_{n-1}\big)$ where $\big({\mathcal{S}}_n = {\mathcal{S}}_{n-1} \bigcup {v}\big)$. Although this heuristic method doesn't guarantee the generation of an optimal subset due to the NP-hard nature of the problem, it can still yield a solution that is assured to be in proximity to the optimal set [@nemhauser1978analysis] and hence approximately solves the submodular maximization problem. Knapsack constraint [@krause_submodular_maximization] is another special case of submodular maximization, where there are different costs associated with adding every new element.
+
+In our work, we have used a generalized graphcut-based monotonous submodular function [@iyer_graph_cut; @submodularity_AI_blimes] that uses a greedy approach to select a set of representative and diverse examples to train the classifier. Further, the selection is done under a knapsack constraint where the addition of any new data sample has a cost equal to the entropy of the pseudo label associated with it, and the selection of samples is done under a budget on the overall entropy of the subset. Eq. [\[eqn:sub\]](#eqn:sub){reference-type="ref" reference="eqn:sub"} illustrates the formulation of the generalized graphcut algorithm. The function $({\mathcal{F}}\big(S\big))$ represents the utility of the set $(S)$, and the variable $s_{i,j}$ represents a kernel function that quantifies the similarity between the $i^{th} \text { and } j^{th}$ samples in the dataset. We have used cosine similarity over the Inception features [@devries2020instance] of the images to define our kernel function. The term $\big(\sum_{i\in \Tilde{{\mathcal{D}}}_t} \sum_{j \in {\mathcal{S}}} {\bm{s}}_{ij}\big)$ represents a cumulative score for the similarity between the samples in set $(S)$ and the data present in the non-abstained dataset ($\Tilde{{\mathcal{D}}}_t$), capturing the representativeness of the selected samples in comparison to $\big(\Tilde{{\mathcal{D}}}_t\big)$. Additionally, the term $\big(\sum_{l,m \in {\mathcal{S}}} {\bm{s}}_{l,m}\big)$ represents the similarity between the selected samples in set $\big(S\big)$, minimizing which, leads to the selection of diverse data points. The hyperparameter $\gamma$ controls the tradeoff between representativeness and diversity. Further, choosing $\gamma \geq 2$ ensures that the given function exhibits monotonicity. $$\begin{align}
+{\mathcal{F}}\big({\mathcal{S}}\big) = \gamma \sum_{i\in \Tilde{{\mathcal{D}}}_t} \sum_{j \in {\mathcal{S}}} {\bm{s}}_{ij} - \sum_{l,m \in {\mathcal{S}}} {\bm{s}}_{l,m}
+\label{eqn:sub}
+\end{align}$$ We have defined a knapsack constraint on the overall entropy of the pseudo labels generated by label model for the selected samples. Under the given constraint, every data sample in $\Tilde{{\mathcal{D}}_t}$ has an associated selection cost equal to the entropy of the pseudo label, and the overall entropy of the selected samples cannot exceed a given budget (${\mathcal{B}}$) (Eq. [\[eqn:budget\]](#eqn:budget){reference-type="ref" reference="eqn:budget"}). This enforces the selection of diverse and representative samples while ensuring that uncertainty associated with the pseudo labels for the selected samples is within a limit. The complete objective of our formulation is stated as follows: $$\begin{align}
+ \max_{{\mathcal{S}}\subset \Tilde{{\mathcal{D}}}_t} {\mathcal{F}}\big({\mathcal{S}}\big) \nonumber \\
+ \text {s.t. Cost (${\mathcal{S}}$)} \leq {\mathcal{B}}
+ \label{eqn:budget}
+\end{align}$$ The budget ${\mathcal{B}}$ on the overall entropy of the pseudo labels for the selected samples is defined to be a fraction ($\eta \text{ where } 0 < \eta < 1$) of the total entropy of the pseudo labels ($Cost(\Tilde{{\mathcal{D}}_t})$) for the non-abstained data set. Eq. [\[Eqn:cost\]](#Eqn:cost){reference-type="ref" reference="Eqn:cost"} illustrates the overall constraint on the given submodular maximization problem, where ${\mathcal{H}}$ is the entropy defined for a given sample ($x_l$), and ${\mathcal{L}}(\Lambda(x_l))$ is the probability distribution of the label model associated with it. $$\begin{align}
+ Cost({\mathcal{S}}) &= \sum_{x_l \in {\mathcal{S}}} {\mathcal{H}}\big(x_l; {\mathcal{L}}(\Lambda(x_l))\big) \nonumber \\
+ Cost(\Tilde{{\mathcal{D}}_t}) &= \sum_{x_l \in \Tilde{{\mathcal{D}}_t}} {\mathcal{H}}\big( x_l; {\mathcal{L}}(\Lambda(x_l))\big) \nonumber \\
+ {\mathcal{B}}&= \eta*Cost(\Tilde{{\mathcal{D}}_t})
+ \label{Eqn:cost}
+\end{align}$$
+
+For the submodular maximization problem under a knapsack constraint, a naive greedy algorithm can perform arbitrarily bad as it is indifferent to the cost associated with each sample. To account for the selection cost associated with every sample, the normal greedy-based selection scheme can further be modified to create a cost-effective greedy algorithm (CEG) (Eq. [\[eqn:ceg\]](#eqn:ceg){reference-type="ref" reference="eqn:ceg"}) to select the optimal set, where $c(.)$ is the cost function defined for every input sample, $v_k$ is the data point that is selected at $k^{th}$ iteration, ${\mathcal{F}}\big({\mathcal{S}}_{k-1}\big)$ is the utility of the set ($S_{k-1}$) as per the the submodular function defined in Eq. [\[eqn:sub\]](#eqn:sub){reference-type="ref" reference="eqn:sub"} and ${\mathcal{S}}_k$ is the new set created by including the data point ${\bm{v}}_k$ in the set ${\mathcal{S}}_{k-1}$. In our formulation, the function $c(.)$ is set to be equal to the entropy associated with the pseudo label generated by label model $\big(c(v) = {\mathcal{H}}(v;{\mathcal{L}}(\Lambda(v)))\big)$. The cost-effective greedy can be summarized as below : $$\begin{align}
+ v_k = \mathop{\mathrm{arg\,max}}_{v\in \Tilde{{\mathcal{D}}_t}/\ {\mathcal{S}}_{k-1}} \frac {{\mathcal{F}}\big({\mathcal{S}}_{k-1} \cup {v}\big) - {\mathcal{F}}\big({\mathcal{S}}_{k-1}\big)} {c(v)} \nonumber \\
+ \text{ where } {\mathcal{S}}_{k} = {\mathcal{S}}_{k-1} \bigcup v_{k}
+ \label{eqn:ceg}
+\end{align}$$ The given algorithm is run iteratively until the total cost of the selected set is less than the predefined budget (${\mathcal{B}}$).
+
+Even though cost-effective greedy considers the selection cost for each sample while selecting a subset, it can still perform arbitrarily bad in some extreme scenarios [@leskovec2007cost]. Fortunately, for the given submodular maximization objective, the cost-effective greedy algorithm can be adapted to provide a constant factor of $\frac{1}{2} \big(1- \frac{1} {e}\big)$ to the optimal solution (Prop. 1 in supplementary material) [@leskovec2007cost; @submodular_knapsack_NAS], hence generating a subset close to the optimal set. In fact, at least one among the final set (${\mathcal{S}}_{uniform}$) selected by using uniform selection cost ($(c(v) = 1)$) and the final set (${\mathcal{S}}_{cost}$) selected using desired cost function $\big(c(v) = {\mathcal{H}}(v;{\mathcal{L}}(\Lambda(v)))\big)$ will have a constant approximation bound with respect to the optimal solution, and the set with the maximum utility score can be used for the training of the classifier ($\Tilde{{\mathcal{D}}}_0 = \mathop{\mathrm{arg\,max}}_{{\mathcal{S}}} ({\mathcal{F}}({\mathcal{S}}_{uniform}),{\mathcal{F}}({\mathcal{S}}_{Cost}) \big)$.
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+# Introduction
+
+Cooperative multi-agent reinforcement learning (MARL) has recently emerged as an exciting research avenue due to its applicability to real-world scenarios. Many of these applications naturally call for multi-agent frameworks, such as resource management [@marl_dis], package delivery [@marl_delivery], disaster rescue [@marl_rescue], and robots control [@marl_robot_swamy2020scaled]. Despite the considerable success of seminal works [@qmix_rashid2020monotonic; @maddpg_lowe2017multi; @mappo_yu2021surprising] in generalizing reinforcement learning algorithms to multi-agent cases under the centralized training and decentralized execution (CTDE) paradigm [@ctde_kraemer2016multi; @ctde_lyu2021contrasting], further improvement in MARL is hindered by a few prominent obstacles, such as non-stationarity and partial observability [@yuan2023survey; @review_coo_oroojlooy2022review].
+
+Subsequent research efforts [@CommNet; @BiCNet] aim to tackle the non-stationary and partially-observable issues in MARL by incorporating communication into the framework. By integrating local information (observations or histories) with messages from other agents, an agent can gain a more comprehensive understanding of the environment, leading to improved decision-making. Prior methods, focusing on improving the MARL task performance, mostly adopt continuous messages and broadcasting mechanisms, thus inducing substantial communication overhead.Communication channels typically face severe bandwidth constraints [@mas_bandwidth_huang2016distributed], making it difficult to directly deploy previous approaches to real-world multi-agent systems (MASs). Therefore, it is crucial to develop MARL communication protocols that can utilize the limited bandwidth more efficiently.
+
+In this work, we consider an MAS with a limited communication budget. Ideally, a communication protocol that operates well under a tight budget shall only allow transmitting information that is useful for the target receiver, i.e., the message generation shall be context-aware. This makes the broadcast communication protocol a suboptimal choice, as different agents likely need different information. But the question remains, i.e., how to identify the pieces of information worth transmitting between two particular agents? Note that MARL aims to solve a decision-making problem at its core, therefore it is up to the receivers to determine what information is relevant and helpful for decision making. This motivates us to develop a receiver-centric communication protocol, in which the receiver initiates the communication by providing the sender with the "context" before the sender can send context-aware messages. We compare ideas of the traditional broadcasting scheme and context-aware communication (CACOM) using an illustrative example in Figure [1](#fig:example){reference-type="ref" reference="fig:example"}. In the first stage of our proposed CACOM, each agent broadcasts a very short context message conveying coarse local information. In the second stage, agents selectively send longer, personalized messages to specific peers based on the context messages they received in the first stage. It is noteworthy that similar communication strategies among humans have been successfully utilized in the fields of marketing [@idea_market_ansari2003customization] and healthcare [@idea_health_kreuter2013tailoring], where they are termed as customized communication.
+
+
+
+An illustrative example in a busy traffic junction. Under the broadcasting communication scheme, without knowledge of agent A and C’s intentions, agent B needs to broadcast all its observations, which will result in heavy communication overhead. In contrast, when adopting context-aware communication, agent A and C first convey their local information in short context messages. Then agent B generates personalized messages for A and C based on the context messages from the previous stage. In this way, more context-relevant messages are provided for decision making with much lower communication overhead.
+
+
+To aid personalized message generation, we treat the entities in agents' observation as tokens and employ an attention-based architecture to encode features and generate personalized messages. This approach enables the network to learn which parts of the features are most important for the intended receiver. Furthermore, we quantize the messages through the LSQ technique [@lsq_esser2020learned] to make the communication blocks compatible to digital communication systems. Since CACOM is agnostic to specific MARL algorithms, we combine it with MADDPG [@maddpg_lowe2017multi] and QMIX [@qmix_rashid2020monotonic] to evaluate its efficacy on cooperative multi-agent benchmarks in communication-constrained scenarios. Specifically, we conduct experiments on two scenarios in multi-agent particle environment (MPE) [@maddpg_lowe2017multi] and four maps in the StarCraft multi-agent challenge (SMAC) [@smac_samvelyan19smac]. The results showcase the superior performance of CACOM in comparison with baselines. Our contributions are summarized as follows:
+
+- We investigate a realistic multi-agent system with limited communication resources and propose a context-aware communication protocol in MARL, namely CACOM, to efficiently utilize the limited communication budgets.
+
+- We leverage various attention blocks to generate personalized messages based on the senders' and receivers' local information, and we incorporate learned step size quantization (LSQ) to ensure digital communication while keeping the overall network differentiable.
+
+- Through adequate experimentation on cooperative multi-agent benchmarks with limited communication budgets, CACOM has been demonstrated to be effective and outperform traditional broadcast communication protocols.
+
+# Method
+
+MADDPG [@maddpg_lowe2017multi] is an actor-critic algorithm that has been commonly adapted for the CTDE paradigm, where a centralized critic $Q\left(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}; \theta \right)$ and individual deterministic policies $\pi_i(o_i, h_i; \theta)$ are learned during the training process ($\theta$ denotes the parameters for deep neural networks). The gradient for the policy network for the $i$-th agent is computed as $$\begin{align}
+ \nabla_{\theta_i}J(\theta_i) = &\mathbb{E}_{(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}, r, \boldsymbol{o}', \boldsymbol{h}') \sim \mathcal{D}} \left[\nabla_{\theta_i}\pi (o_i, h_i; \theta_{i}) \right. \nonumber\\
+ &\left.\cdot \nabla_{a_i}Q(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}_{-i}, a_i ; \theta)|_{a_{i}=\pi_i(o_i, h_i; \theta_i)} \right],
+\end{align}$$ while the centralized critic is updated using the temporal difference (TD) learning objective $$\begin{align}
+ \mathcal{L}_{\text{TD}}(\theta) = \mathbb{E}_{(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}, r, \boldsymbol{o}', \boldsymbol{h}') \sim \mathcal{D}} \left[\left(y - Q\left(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}; \theta \right) \right)^2 \right],
+\end{align}$$ where $y = r + \gamma Q\left(\boldsymbol{o}, \boldsymbol{h}', \boldsymbol{a}' ; \theta^-\right)|_{a_{i}' {=}\pi_i(o_i', h_i'; \theta_i^-)},$ and $\theta^-$ denotes the target networks with delayed parameters.
+
+QMIX [@qmix_rashid2020monotonic] is a multi-agent value-based algorithm that exploits the idea of value decomposition. In this algorithm, the global value function $Q_{\text{tot}}(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}; \theta)$ is decomposed into individual value functions $Q_{i}(o_i, h_i, a_i; \theta_i)$ following the monotonic principle. The training objective is to minimize the TD error, which is computed as $$\begin{align}
+ \mathcal{L}_{\text{TD}}(\theta) = \mathbb{E}_{(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}, r, \boldsymbol{o}', \boldsymbol{h}') \sim \mathcal{D}} \left[ \left(y - Q_{\text{tot}}(\boldsymbol{o}, \boldsymbol{h}, \boldsymbol{a}; \theta) \right)^2 \right],
+\end{align}$$ where $y = r + \gamma \max_{\boldsymbol{a}'} Q_{\text{tot}}(\boldsymbol{o}', \boldsymbol{h}', \boldsymbol{a}'; \theta^-).$
diff --git a/2401.11800/main_diagram/main_diagram.drawio b/2401.11800/main_diagram/main_diagram.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..5e217a5d3d08e5cf2a2a0beb5af27533047b6479
--- /dev/null
+++ b/2401.11800/main_diagram/main_diagram.drawio
@@ -0,0 +1 @@
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ldFBD4IgFADgX8OxTaGaHRuldmhrs82uTEjZ0GdIy/r12ZCMdakLg4/Hg/dAhNZ9ollb7YELhXDAe0Q2CONogYfxBXcL82hpodSSWwonyORDjBiMepVcdF6gAVBGtj4W0DSiMJ4xreHmh51B+be2rBRfkBVMfWsuualcWcHkqZBl5W4Og3GnZi54hK5iHG4fRLaIUA1g7KzuqVCv3rm+XJJoR1drxaP4mNNTup8f4plNFv9z5F2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+# Introduction
+
+Relation extraction is the task of extracting semantic links or connections between entities from an input text [@baldini-soares-etal-2019-matching]. In recent years, document-level relation extraction problem (DocRE) evolved as a new subtopic due to the widespread use of relational knowledge in knowledge graphs [@yu-etal-2017-improved] and the inherent manifestation of cross-sentence relations involving multi-hop reasoning. Thus, as compared to traditional RE, DocRE has two major challenges: subject and object entities in a given triple might be dispersed across distinct sentences, and certain entities may have aliases in the form of distinct entity mentions. Consequently, the signal (hints) needed for DocRE is not confined to a single sentence. A common approach to solve this problem is by taking the input sentences and constructing a structured graph based on syntactic trees, co-references, or heuristics to represent relation information between all entity pairs [@nan-etal-2020-reasoning]. A graph neural network model is applied to the constructed graph, which performs multi-hop graph convolutions to derive features of the involved entities. A classifier uses these features to make predictions [@DBLP:conf/aaai/ZhangCZW20]. Another approach [@xu-etal-2021-discriminative; @10.1145/3572898] explicitly models the reasoning process of different reasoning skills (e.g., multi-hop, coreference-mediated). However, even after considering features between the entity pairs and executing the reasoning process, DocRE is still hard due to the latent and unspecific/imprecise contexts.
+
+Consider the sentence in Figure [1](#example){reference-type="ref" reference="example"} and its labeled relation *applies_to_jurisdiction* (Congress, US) from the DocRED dataset [@yao-etal-2019-docred]. Even with the inclusion of multi-hop and co-reference reasoning, inferring the correct relation becomes challenging because the relation depends on multiple sentences and cannot be identified based on the language used in the sentence.
+
+
+
+A partial document and labeled relation from DocRED. Blue color represents concerned entities, pink color represents other mentioned entities, and yellow color denotes the sentence number.
+
+
+For these kinds of sentences, external context (knowledge) can play a vital role in helping the model capture more about the involved entities. For the above example, using the Wikidata knowledge base [@10.1145/2629489] and WordNet [@10.1145/219717.219748], we can get details, such as the entity types, synonyms, and other direct and indirect relations between entities (if they exist) on the Web. For these kinds of sentences, external context (knowledge) can play a vital role in helping the model capture more about the involved entities. For the above example, using the Wikidata knowledge base [@10.1145/2629489], we can get additional details, such as the entity types, synonyms, and other direct and indirect relations between entities (if they exist) on the Web.
+
+Previous research in this domain has underscored the potential of external context to enhance performance in relation extraction, co-reference resolution, and named entity recognition [@shimorina-etal-2022-knowledge]. The distinctive innovation of our work lies in the fusion of context extracted from Wikidata and WordNet with a reasoning framework, enabling the prediction of entity relationships based on input document observations. Given the wide availability of external context, Knowledge Graph (KG) triples can augment training data, thereby casting the DocRE task as a knowledge Graph based link prediction challenge. In other words, given head and tail entities, we address the question of determining the appropriate relation.
+
+We demonstrate that framing DocRE as a link prediction problem, combined with contextual knowledge and reasoning, yields enhanced accuracy in predicting the relation between entities. We furnish traversal paths as compelling justifications for relation predictions, thereby shedding light on why a particular relation is favored over others. Notably, this marks the first instance of presenting a traversal path between entities for each prediction in the context of DocRE. Our contributions in this work are as follows.
+
+- We introduce an innovative approach named DocRE-CLiP (**Doc**ument-level **R**elation **E**xtraction with **C**ontext-guided **Li**nk **P**rediction), which amalgamates external entity context with reasoning via a link prediction algorithm.
+
+- Our empirical analyses encompass three widely-used public document-level relation extraction datasets, showcasing our model's improvement over the recent state-of-the-art methods.
+
+- Significantly, for every prediction, our approach is first in DocRE literature to supply a traversal path as corroborative evidence, bolstering the model's interpretability.
+
+# Method
+
+An unstructured document D consisting of K sentences is represented by $\{S\}_{i=1}^{k}$, where each sentence is a sequence of words and entities $\mathcal{E}$=$\{{e_i}\}_{i=1}^{P}$(P is the total number of entities). Entities $e_{i}$ has multiple mentions, $m_{i}^{s_{k}}$, scattered across the document D. Entity alias are represented as $\{{m_{i}^{s_{k}}}\}_{k=1}^{Q}$. Our objective is to extract the relation between two entities in $\mathcal{E}$ namely P(r$\mid$e$_i$,e$_j$) where $e_i,e_j\in \mathcal{E}, r\in \mathcal{R}$, here $\mathcal{R}$ is a total labeled relation set. The context (background knowledge) of an entity ${e_i}$ is represented by $C_{e_i}$ and a context path, i.e., a sequence of connected entities and edges from the head entity (e$_i$) to the tail entity (e$_j$) is represented by $CP_{e_i,e_j}$.
+
+Our proposed framework, DocRE-CLiP, integrates document-derived reasoning with context knowledge using link prediction. In the first step, we extract triples from the sentences of the given document. In the second step, we extract two types of context: 1) entity context, such as its aliases, and 2) context paths from an external KB (Wikidata, in this case). Using the triples and extracted contexts, we create a context graph to calculate a link prediction score. Then in the third step, we use several reasoning mechanisms such as logical reasoning, intra-sentence reasoning, and co-reference reasoning to calculate relation scores for pairs of entities. In the final step, the aggregation module combines the relation scores from the second and third steps. We have also implemented a path-based beam search in the framework to explain the predicted relation by providing traversal paths based on scores (refer to Figure [3](#DocRE-CLiP){reference-type="ref" reference="DocRE-CLiP"}). We now detail the architecture of our proposed framework.
+
+Document Relation Extraction (DocRE) datasets often contain labeled triplets; however, popular datasets [@yao-etal-2019-docred] have about 64.6% missing triples, yielding an incomplete graph [@DBLP:conf/emnlp/Tan0BNA22]. To extract all the triples from the document, we utilize an open-source state-of-the-art method [@huguet-cabot-navigli-2021-rebel-relation] for triplet generation. This model is chosen based on source code availability and run time. This module takes a document $D$ as input and produces triples ($s$, $p$, $o$), where $s$ is the head entity, $p$ is the relation, and $o$ is the tail entity. These extracted triples ($T$) follow the equation below, where $n$ is the total count of triples extracted from document $D$. $$\begin{equation}
+\label{eq3}
+ T(D)=\{s_i,p_i,o_i\}_{i=1}^n
+\end{equation}$$
+
+Our goal involves extracting two types of contexts -- entity context and the contextual path between entity pairs. For entity $e_i$, we generate entity context using entity type and synonyms, which are derived by using WordNet [@10.1145/219717.219748]. We incorporate the entity context $C_{e_i}$ into the triples. Here, $S$ denotes the total number of extracted synonyms. Refer to equations [\[synonym\]](#synonym){reference-type="ref" reference="synonym"} and [\[type\]](#type){reference-type="ref" reference="type"} for more details.
+
+$$\begin{equation}
+\label{synonym}
+C_{e_i}=\{e_i, hasSynonym, Synonym_k\}_{k=1}^S,
+\end{equation}$$ $$\begin{equation}
+\label{type}
+C_{e_i}=\{ e_i, hasEntityType,{EntityType}\}
+\end{equation}$$
+
+Let us consider an entity "USA" as an example. When we utilize WordNet, we discover that synonyms for "US" include, "America", and "United States". Additionally, the entity is categorized as the type "Country". Consequently, the following set of triples is generated through this process:
+
+::: tcolorbox
+*{USA, hasSynonym, US}*\
+*{USA, hasSynonym, America}*\
+*{USA, hasSynonym, United States}*\
+*{USA, hasEntityType, Country}*\
+:::
+
+The second source of contextual information pertains to entity paths. Predicting relations between entity pairs poses challenges stemming from inherent document deficiencies [@DBLP:conf/emnlp/Tan0BNA22]. To address these issues, we introduced external context by harnessing insights from Wikidata. The procedure involves extracting paths (direct and indirect) between entity-entity, mention-entity, and entity-mention pairs from Wikidata, provided they exist. The contextual path pertains to an entity pair ${{e_i,e_j}}$. We considered context paths spanning an N-hop distance (N being chosen based on experimental findings) between the entity pair. Subsequently, the extracted path is transformed into triples and forwarded to the link prediction model.
+
+Illustrated in Figure [2](#hopdistance){reference-type="ref" reference="hopdistance"} is the triple generation process employing a contextual path. The entity *Canadian* is two hops away from the entity *Ontario*. *Canada* is an intermediary entity, while *country* and *ethnic group* are intermediary properties. The Contextual Path ($CP_{e_i,e_j}$) are a set of triples formed using the intermediary entities and properties, as shown in Figure [2](#hopdistance){reference-type="ref" reference="hopdistance"}.
+
+
+
+Triples constructed using N-hop path extracted from Wikidata. The head and tail entities are blue in color. Intermediate entities are in peach color.
+
+
+Link prediction is the task of predicting absent or potential connections among nodes within a network [@10.1145/956863.956972]. Given that the document relation extraction (DocRE) task involves constructing a graph that interlinks entities and considering our context, formulated as triples, which can be conceptualized as a Knowledge Graph (KG), we approach the DocRE challenge as a link prediction problem. This approach encompasses both an encoder and a decoder. The encoder maps each entity $e_{i} \in \mathcal{E}$ to a real-valued vector $v_i \in \mathbb{R}^{d}$, where $\mathbb{R}$ denotes the set of real-valued relation vectors of dimension $d$. The decoder reconstructs graph edges by leveraging vertex representations, essentially scoring (subject, relation, object) triples using a function: $\mathbb{R}^{d} \times \mathcal{R} \times \mathbb{R}^{d} \rightarrow \mathbb{R}$. While prior methods often employ a solitary real-valued vector $e_i$ $\in \mathcal{E}$, our approach computes representations using an R-GCN [@DBLP:conf/esws/SchlichtkrullKB18] encoder, where $h_i^{(l)}$ is the hidden state of node $e_i$ in the l-th layer of the neural network. To compute the forward pass for an entity $e_i$ in a relational multi-graph, the propagation model at layer $l+1$ is computed as follows. $$\begin{equation}
+h_i^{(l+1)}=\sigma \left(\sum_{r\in{R}}\sum_{j\in{N_i^{r}}}(\frac{1}{c_{i,r}}W_r^{(l)}h_j^{(l)}+W_0^{(l)}h_{i}^{(l)}) \right)
+\end{equation}$$
+
+Here, $h_{i}^{(l)}$ $\in \mathbb{R}^{d^{(l)}}$ with $d^{(l)}$ being the dimensionality of layer $l$. $W_0^{l}$ and $W_r^{(l)}$ represent the block diagonal weight matrices of the neural network, and $\sigma$ represents the activation function. $N_i^{r}$ signifies the set of neighboring indices of node $i$ under relation $r \in R$, and $c_{i,r}$ is a normalization constant.
+
+For training the link prediction model, our dataset comprises a) core training triples from the dataset, b) triplets obtained through the triplet extraction module using Equation [\[eq3\]](#eq3){reference-type="ref" reference="eq3"}, c) triplets formulated using the context module guided by Equations [\[synonym\]](#synonym){reference-type="ref" reference="synonym"} and [\[type\]](#type){reference-type="ref" reference="type"}, and d) triplets constructed using context paths connecting entity pairs. We use DistMult [@DBLP:journals/corr/YangYHGD14a] as the decoder. It performs well on the standard link prediction benchmarks. Every relation r in a triple is scored using equation [\[eq8\]](#eq8){reference-type="ref" reference="eq8"}.
+
+$$\begin{equation}
+\label{eq8}
+ P(r \mid i,j)=P(e_i^T \times R_r \times e_j)
+\end{equation}$$
+
+We consider three types of reasoning in our approach.\
+1) *Intra-sentence reasoning*, which is a combination of pattern recognition and common sense reasoning. Intra-sentence reasoning path is defined as $P I_{i j}=m_{i}^{s_{1}} \circ s_{1} \circ m_{j}^{s_{1}}$ for entity pair {$e_i,e_j$} insider the sentence $s_1$ in document D. $m_i^{s_1}$ and $m_j^{s_1}$ are mentions and "o" denotes reasoning step on reasoning path from $e_i$ to $e_j$.\
+2) *Logical reasoning* is where a bridge entity indirectly establishes the relations between two entities. Logical reasoning path is formally denoted as $P L_{i j}=m_{i}^{s_{1}} \circ s_{1} \circ m_{l}^{s_{1}} \circ m_{l}^{s_{2}} \circ \ s_{2}\ \circ$ $\ m_{j}^{s_{2}}$ for entity pair {$e_i,e_j$} from sentence $s_1$ and $s_2$ is directly established by bridge entity $e_l$.\
+3) *Co-reference reasoning* which is nothing but co-reference resolution. Co-reference reasoning path is defined as $PC_{ij}=m_{i}^{s_{1}} \circ s_{1} \circ s_{2} \circ m_{j}^{s_{2}}$ between two entities $e_i$ and $e_j$ which occur in same sentence as other entity. Our implementation of these reasoning skills is inspired by [@xu-etal-2021-discriminative].
+
+Consider an entity pair $\{e_i,e_j\}$ and its intra sentence reasoning path (PI$_{i j}$), logical reasoning path (PL$_{i j}$) and co-reference reasoning path (PC$_{i j}$) in the sentence. The various reasoning is modeled to recognize the entity pair as intra-sentence reasoning $\mathrm{R}_{P I}(r)=P\left(r \mid e_{i}, e_{j}, P I_{i j}, D\right)$, logical reasoning $\mathrm{R}_{P L}(r)=P\left(r \mid e_{i}, e_{j}, P L_{i j}, D\right)$ and co-reference reasoning $\mathrm{R}_{P C}(r)=P\left(r \mid e_{i}, e_{j}, P C_{i j}, D\right)$. Reasoning type is selected with max probability to recognize the relation between each entity pair using the equation: $$\begin{equation}
+ P\left(r \mid e_{i}, e_{j}, D\right)=\max \left[\mathrm{R}_{P I}(r), \mathbf{R}_{P L}(r), \mathbf{R}_{P C}(r)\right]
+\end{equation}$$
+
+
+
+ Illustration of proposed framework DocRE-CLiP and its various modules.
+
+
+For discerning relations between two entities, we employ two categories of context representation -- heterogeneous graph context representation (HGC) and document-level context representation (DLC) to model diverse reasoning paths [@DBLP:conf/aaai/Zhou0M021]. In heterogeneous graph context representation (HGC), a word is portrayed as a concatenation embedding of its word ($W_e$), entity type ($W_t$), and co-reference embedding ($W_c$). This composite embedding is then input into a BiLSTM to convert the document $D$ into a vectorized form using the equation: BiLSTM(\[W$_e$:W$_t$:W$_c$\]). Following the methodology of [@zeng-etal-2015-distant], a heterogeneous graph is constructed based on sentence and mention nodes. For document-level context representation, following [@DBLP:conf/icra/EisenbachLAG23], a self-attention mechanism is employed to learn document-level context (DLC) for a specific mention based on the vectorized input document $D$.
+
+To model intra-sentence reasoning path ($\alpha_{i j})$, logical reasoning path ($\beta_{i j}$) and co-reference reasoning path ($\gamma_{i j}$), HGC and DLC representation are combined [@DBLP:conf/aaai/XuCZ21]. These reasoning representations are the input to the classifier to compute the probabilities of the relation between $e_i$ and $e_j$ entities by a multi-layer perceptron (MLP) for each path, respectively (equation [\[eq2\]](#eq2){reference-type="ref" reference="eq2"}).
+
+$$\begin{equation}
+\label{eq2}
+ P\left(r \mid e_{i}, e_{j}\right) = \max\left[
+ \begin{aligned}
+ &\operatorname{sigmoid}\left(\operatorname{MLP}_{r}\left(\alpha_{ij}\right)\right), \\
+ &\operatorname{sigmoid}\left(\operatorname{MLP}_{r}\left(\beta_{ij}\right)\right), \\
+ &\operatorname{sigmoid}\left(\operatorname{MLP}_{r}\left(\gamma_{ij}\right)\right)
+ \end{aligned}
+ \right]
+\end{equation}$$
+
+By the end of this step, we will get the score of each relation ($r$) for a given $\{e_i,e_j\}$.
+
+In this module, we aggregate the probability score from the reasoning module equation [\[eq2\]](#eq2){reference-type="ref" reference="eq2"} and link prediction probability score using equation [\[eq8\]](#eq8){reference-type="ref" reference="eq8"}. Further, the binary cross-entropy is used as a training objective function [@yao-etal-2019-docred] for predicting the final relation.
+
+An essential component of our approach is that it can explain the predicted relation by providing the most relevant path in the graph between the given entity pairs. This represents a notable advancement, as contemporary state-of-the-art models cannot often furnish explanations alongside their predictions. Unlike Greedy search, where each position is assessed in isolation, and the best choice is selected without considering preceding positions, we use beam search. This strategy selects the top "N" sequences thus far and factors in probabilities involving the concatenation of all previous paths and the path in the current position.
+
+Inspired by [@10.1145/3514221.3517887], we used beam search to derive plausible paths leading to the target entity within a graph. This graph (G) is constructed using the triples to train the link prediction module, augmented by test result triplets from the model's predictions. Our objective is to create a comprehensive graph encompassing the maximum available details, to generate substantial explanations for the predictions. Formally, we conceptualize the path-based beam search challenge as follows. Given a structured relational query ($e_i$, $r$, ?), where $e_i$ serves as the head entity, $r$ signifies the query relation, and ($e_i$, $r$, $e_j$) $\in$ G, our objective is to identify a collection of plausible answer entities e$_j$ by navigating paths through the existing entities and relations within G, leading to tail entities. We compile a list of distinct entities reached during the final search step and assign the highest score attained among all paths leading to each entity. Subsequently, we present the top-ranked unique entities. This approach surpasses the direct output of entities ranked at the beam's apex, which often includes duplicates. Upon completing this step, we obtain actual paths (sequences of nodes and edges) for enhanced interpretability.
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diff --git a/2402.02464/paper_text/intro_method.md b/2402.02464/paper_text/intro_method.md
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+# Introduction
+
+Graphs, inherent to Non-Euclidean data, are extensively applied in scientific fields such as molecular design, social network analysis, recommendation systems, and meshed 3D surfaces (Shakibajahromi et al., 2024; Zhou et al., 2020a; Huang et al., 2022; Tan et al., 2023; Li et al., 2023a; Liu et al., 2023a; Xia et al., 2022b;b; Gao et al., 2022a; Wu et al.,
+
+*Proceedings of the* 41 st *International Conference on Machine Learning*, Vienna, Austria. PMLR 235, 2024. Copyright 2024 by the author(s).
+
+2024a;b; Tan et al., 2023; Gao et al., 2022a;b; 2023; Lin et al., 2022a). The Non-Euclidean nature of graphs has inspired sophisticated model designs, including graph neural networks (Kipf & Welling, 2016a; Velickovi ˇ c et al. ´ , 2017) and graph transformers (Ying et al., 2021; Min et al., 2022). These models excel in encoding graph structures through attention maps. However, the structural encoding strategies limit the usage of auto-regressive mechanism, thereby hindering pure transformer from revolutionizing graph fields, akin to the success of Vision Transformers (ViT) (Dosovitskiy et al., 2020) in computer vision. We employ pure transformer for graph modeling and address the following open questions: *(1) How to eliminate the Non-Euclidean nature to facilitate graph representation? (2) How to generate Non-Euclidean graphs from Euclidean representations? (3) Could the combination of graph representation and generation framework benefits from self-supervised pretraining?*
+
+We present Graph2Seq, a pure transformer encoder designed to compress the Non-Euclidean graph into a sequence of learnable tokens called Graph Words in a Euclidean form, where all nodes and edges serve as the inputs and undergo an initial transformation to form Graph Words. Different from graph transformers (Ying et al., 2021), our approach doesn't necessitate explicit encoding of the adjacency matrix and edge features in the attention map. Unlike TokenGT (Kim et al., 2022), we introduce a Codebook featuring learnable vectors for graph position encoding, leading to improved training stability and accelerated convergence. In addition, we employ a random shuffle of the position Codebook, implicitly augmenting different input orders for the same graph, and offering each position vector the same opportunity of optimization to generalize to larger graphs.
+
+We introduce GraphGPT, a groundbreaking GPT-style transformer model for graph generation. To recover the Non-Euclidean graph structure, we propose an edge-centric generation strategy that utilizes block-wise causal attention to sequentially generate the graph. Contrary to previous methods (Hu et al., 2020a; Shi et al., 2019; Peng et al., 2022) that generate nodes before predicting edges, the edge-centric technique jointly generates edges and their corresponding endpoint nodes, greatly simplifying the generative space. To align graph generation with language generation, we implement auto-regressive generation using block-wise causal attention, which enables the effective translation of Euclidean
+
+\*Equal contribution 1Westlake University, Hangzhou, China 2Zhejiang University, Hangzhou, China. Correspondence to: Stan Z. Li .
+
+representations into Non-Euclidean graph structures.
+
+Leveraging Graph2Seq encoder and GraphGPT decoder, we present GraphsGPT, an integrated end-to-end framework. This framework facilitates a natural self-supervised task to optimize the representation and generation tasks, enabling the transformation between Non-Euclidean and Euclidean data structures. We pretrain GraphsGPT on 100M molecule graphs and comprehensively evaluate it from three perspectives: Encoder, Decoder, and Encoder-Decoder. The pretrained Graph2Seq encoder is a strong graph learner for property prediction, outperforming baselines of sophisticated methodologies on 8/9 molecular classification and regression tasks. The pretrained GraphGPT decoder serves as a powerful structure prior, showcasing both few-shot and conditional generation capabilities. The GraphsGPT framework seamlessly connects the Non-Euclidean graph space to the Euclidean vector space while preserving information, facilitating tasks that are known to be challenging in the original graph space, such as graph mixup. The good performance of pretrained GraphsGPT demonstrates that our edge-centric GPT-style pretraining task offers a simple yet powerful solution for graph learning. In summary, we tame pure transformer to convert Non-Euclidean graph into K learnable Graph Words , showing the capabilities of Graph2Seq encoder and GraphGPT decoder pretrained through self-supervised tasks, while also paving the way for various Non-Euclidean challenges like graph manipulation and graph mixing in Euclidean latent space.
+
+# Method
+
+Figure 1 outlines the comprehensive architecture of **GraphsGPT**, which consists of a **Graph2Seq** encoder and a **GraphGPT** decoder. The **Graph2Seq** converts Non-Euclidean graphs into a series of learnable feature vectors, named Graph Words. Following this, the **GraphGPT** utilizes these Graph Words to auto-regressively reconstruct the original Non-Euclidean graph. Both components, the **Graph2Seq** and **GraphGPT**, incorporate the pure transformer structure and are pretrained via a GPT-style pretext task.
+
+Flexible Token Sequence (FTSeq). Denote $\mathcal{G}=(\mathcal{V},\mathcal{E})$ as the input graph, where $\mathcal{V}=\{v_1,\cdots,v_n\}$ and $\mathcal{E}=\{e_1,\cdots,e_{n'}\}$ are sets of nodes and edges associated with features $\mathbf{X}^{\mathcal{V}}\in\mathbb{R}^{n,C}$ and $\mathbf{X}^{\mathcal{E}}\in\mathbb{R}^{n',C}$ , respectively. With a
+
+slight abuse of notation, we use $e_i^l$ and $e_i^r$ to represent the left and right endpoint nodes of edge $e_i$ . For example, we have $e_1 = (e_1^l, e_1^r) = (v_1, v_2)$ in Figure 2. Inspired by (Kim et al., 2022), we flatten the nodes and edges in a graph into a Flexible Token Sequence (FTSeq) consisting of:
+
+- 1. **Graph Tokens**. The stacked node and edge features are represented by $\mathbf{X} = [\mathbf{X}^{\mathcal{V}}; \mathbf{X}^{\mathcal{E}}] \in \mathbb{R}^{n+n',C}$ . We utilize a token Codebook $\mathcal{B}_t$ to generate node and edge features, incorporating 118+92 learnable vectors. Specifically, we consider the atom type and bond type, deferring the exploration of other properties, such as the electric charge and chirality, for simplicity.
+- 2. **Graph Position Encodings (GPE)**. The graph structure is implicitly encoded through decoupled position encodings, utilizing a position Codebook $\mathcal{B}_p$ comprising m learnable embeddings $\{o_1, o_2, \cdots, o_m\} \in \mathbb{R}^{m,d_p}$ . The position encodings of node $v_i$ and edge $e_i$ are expressed as $\mathbf{g}_{v_i} = [\mathbf{o}_{v_i}, \mathbf{o}_{v_i}]$ and $\mathbf{g}_{e_i} = [\mathbf{o}_{e_i^l}, \mathbf{o}_{e_i^r}]$ , respectively. Notably, $\mathbf{g}_{v_i}^l = \mathbf{g}_{v_i}^r = \mathbf{o}_{v_i}$ , $\mathbf{g}_{e_i}^l = \mathbf{o}_{e_i^l}$ , and $\mathbf{g}_{e_i}^r = \mathbf{o}_{e_i^r}$ . To learn permutation-invariant features and generalize to larger, unseen graphs, we *randomly shuffle* the position Codebook, giving each vector an equal optimization opportunity.
+- 3. **Segment Encodings (Seg)**. We introduce two learnable segment tokens, namely [node] and [edge], to designate the token types within the FTSeq.
+
+Require: Canonical SMILES CS.
+
+Ensure: Flexible Token Sequence FTSeq.
+
+1: Convert canonical SMILES CS to graph $\mathcal{G}$ .
+
+2: Get the first node $v_1$ in graph $\mathcal{G}$ by CS.
+
+3: Initialize sequence FTSeq = $[v_1]$ .
+
+4: for $e_i$ in DFS( $\mathcal{G}, v_1$ ) do
+
+5: Update sequence FTSeq $\leftarrow$ [FTSeq, $e_i$ ].
+
+6: if $e_i^r$ not in FTSeq then
+
+7: Update sequence FTSeq $\leftarrow$ [FTSeq, $e_i^r$ ].
+
+8: end if
+
+9: end for
+
+
+
+Figure 2: Graph to Flexible Sequence.
+
+As depicted in Figure 2, we utilize the Depth-First Search (DFS) algorithm to convert a graph into a flexible token sequence, denoted as $FTSeq = [v_1, e_1, v_2, e_2, v_3, e_3, v_4, e_4]$ , where the starting atom matches that in the canonical SMILES. Algorithm 1 provides a detailed explanation of our approach. It is crucial to emphasize that the resulting FTSeq remains Non-Euclidean data, as the number of nodes and edges may vary across different graphs.
+
+**Euclidean Graph Words.** Is there a Euclidean representation that can completely describe the Non-Euclidean graph? Given the FTSeq and k graph prompts $[[GP]_1, [GP]_2, \cdots, [GP]_k]$ , we use pure transformer to learn a set of Graph Words $\mathcal{W} = [w_1, w_2, \cdots, w_k]$ :
+
+$$W = Graph2Seq([GP]_1, [GP]_2, \cdots, [GP]_k, FTSeq]),$$
+(1)
+
+The token $[GP]_k$ is the sum of a learnable [GP] token and the k-th position encoding. The learned Graph Words $\mathcal{W}$ are ordered and of fixed length, analogous to a novel graph language created in the latent Euclidean space.
+
+**Graph Vocabulary.** In the context of a molecular system, the complete graph vocabulary for molecules encompasses:
+
+- 1. The Graph Word prompts [GP];
+- Special tokens, including the begin-of-sequence token [BOS], the end-of-sequence token [EOS], and the padding token [PAD];
+- 3. The dictionary set of atom tokens $\mathcal{D}_v$ with a size of $|\mathcal{D}_v|=118$ , where the order of atoms is arranged by their atomic numbers, e.g., $\mathcal{D}_6$ is the atom C;
+- 4. The dictionary set of bond tokens $\mathcal{D}_e$ with a size of $|\mathcal{D}_e| = 92$ , considering the endpoint atom types, e.g., C-C and C-O are different types of bonds even though they are both single bonds.
+
+How to ensure that the learned Graph Words are informationequivalent to the original Non-Euclidean graph? Previous graph self-supervised learning methods focused on subgraph generation and multi-view contrasting, which suffer potential information loss due to insufficient capture of the global graph topology. In comparison, we adopt a GPT-style decoder to auto-regressively generate the whole graph from the learned Graph Words in a edge-centric manner.
+
+**GraphGPT Formulation.** Given the learned Graph Words $\mathcal{W}$ and the flexible token sequence FTSeq, the complete data sequence is $[\mathcal{W}, [BOS], FTSeq] = [\boldsymbol{w}_1, \boldsymbol{w}_2, \cdots, \boldsymbol{w}_k, [BOS], v_1, e_1, v_2, \cdots, e_i]$ . We define FTSeq1:i as the sub-sequence comprising edges with connected nodes up to $e_i$ :
+
+$$\texttt{FTSeq}_{1:i} = \begin{cases} [v_1, e_1, \cdots, e_i, e_i^r], & \text{if } e_i^r \text{ is a new node} \\ [v_1, e_1, \cdots, e_i], & \text{otherwise} \end{cases} . \tag{2}$$
+
+In an edge-centric perspective, we assert $e_i^r$ belongs to $e_i$ . If $e_i^r$ is a new node, it will be put after $e_i$ . Employing GraphGPT, we auto-regressively generate the complete FTSeq conditioned on $\mathcal{W}$ :
+
+$$\mathtt{FTSeq}_{1:i+1} \xleftarrow{\mathtt{FTSeq}_{1:i}} \mathtt{GraphGPT}([\mathcal{W}, \mathtt{[BOS]}, \mathtt{FTSeq}_{1:i}]), \tag{3}$$
+
+where the notation above the left arrow signifies that the output $FTSeq_{1:i+1}$ corresponds to $FTSeq_{1:i}$ .
+
+**Edge-Centric Graph Generation.** Nodes and edges are the basic components of a graph. Traditional node-centric graph generation methods divide the problem into two parts:
+
+We argue that node-centric approaches lead to imbalanced difficulties in generating new nodes and edges. For the molecular generation, let $|\mathcal{D}_v|$ and $|\mathcal{D}_e|$ denote the number of node and edge types, respectively. Also, let n and n' represent the number of nodes and edges. The stepwise classification complexities for predicting the new node and edge are $\mathcal{O}(|\mathcal{D}_v|)$ and $\mathcal{O}(n \times |\mathcal{D}_e|)$ , respectively. Notably, we observe that $\mathcal{O}(n \times |\mathcal{D}_e|) \gg \mathcal{O}(|\mathcal{D}_v|)$ , indicating a pronounced imbalance in the difficulties of generating nodes and edges. Considering that $\mathcal{O}(|\mathcal{D}_v|)$ and $\mathcal{O}(|\mathcal{D}_e|)$ are constants, the overall complexity of node-centric graph generation is $\mathcal{O}(n+n^2)$ .
+
+These approaches ignore the basic truism that naturally occurring and chemically valid bonds are sparse: there are only 92 different bonds (considering the endpoints) among 870M molecules in the ZINC database (Irwin & Shoichet, 2005). Given such an observation, we propose the edge-centric generation strategy that decouples the graph generation into:
+
+We provide a brief illustration of the three steps in Figure 3. The step-wise classification complexity of generating an edge is $\mathcal{O}(|\mathcal{D}_e|)$ . Once the edge is obtained, the model automatically infers the left node attachment and right node placement, relieving the generation from the additional bur-
+
+
+
+
+
+Figure 3: Overview of edge-centric graph generation.
+
+den of generating atom types and edge connections, resulting in a reduced complexity of $\mathcal{O}(1)$ . With edge-centric generation, we balance the classification complexities of predicting nodes and edge as constants. Notably, the overall generation complexity is reduced to $\mathcal{O}(n+n')$ .
+
+Next, we introduce the edge-centric generation in detail.
+
+**Step 0: First Node Initialization.** The first node token of FTSeq is generated by:
+
+$$\begin{cases} \boldsymbol{h}_{v_1} \xleftarrow{[\texttt{BOS}]} \operatorname{GraphGPT}([\mathcal{W}, [\texttt{BOS}]]) \\ \boldsymbol{p}_{v_1} = \operatorname{Pred}_v(\boldsymbol{h}_{v_1}) \\ v_1 = \operatorname{arg\,max} \boldsymbol{p}_{v_1} \quad \operatorname{Node\,Type} \\ \boldsymbol{g}_{v_1} = [\boldsymbol{o}_1, \boldsymbol{o}_1] \quad \operatorname{GPE} \end{cases}$$
+(4)
+
+Here, $\operatorname{Pred}_v(\cdot)$ denotes a linear layer employed for the initial node generation, producing a predictive probability vector $\boldsymbol{p}_{v_1} \in \mathbb{R}^{|\mathcal{D}_v|}$ . The output $v_1$ corresponds to the predicted node type, and $\boldsymbol{o}_1$ represents the node position encoding retrieved from the position Codebook $\mathcal{B}'_p$ of the decoder, where we should explicitly note that the encoder Codebook $\mathcal{B}_p$ and the decoder Codebook $\mathcal{B}'_p$ are not shared.
+
+**Step 1: Next Edge Generation.** The edge-centric graph generation method creates the next edge by:
+
+$$\begin{cases} \boldsymbol{h}_{e_{i+1}} \xleftarrow{e_i} \operatorname{GraphGPT}([\mathcal{W}, \texttt{[BOS]}, \texttt{FTSeq}_{1:i}]) \\ \boldsymbol{p}_{e_{i+1}} = \operatorname{Pred}_e(\boldsymbol{h}_{e_{i+1}}) \\ e_{i+1} = \operatorname{arg} \max \boldsymbol{p}_{e_{i+1}} & \text{Edge Type} \end{cases} ,$$
+
+where $\operatorname{Pred}_e$ is a linear layer for the next edge prediction, and $\boldsymbol{p}_{e_{i+1}} \in \mathbb{R}^{|\mathcal{D}_e|+1}$ is the predictive probability. $e_{i+1}$ belongs to the set $\mathcal{D}_e \cup \{ [EOS] \}$ , and the generation process
+
+will stop if $e_{i+1} = [EOS]$ . Note that the edge position encoding $[o_{e_{i+1}^l}, o_{e_{i+1}^r}]$ remains undetermined. This information will affect the connection of the generated edge to the existing graph, as well as the determination of new atoms, i.e., left atom attachment and right atom placement.
+
+**Training Token Generation.** The first node and next edge prediction tasks are optimized by the cross entropy loss:
+
+$$\mathcal{L}_{\text{token}} = -\sum_{i} y_i \cdot \log p_i. \tag{6}$$
+
+Step 2: Left Node Attachment. For the newly predicted edge $e_{i+1}$ , we further determine how it connects to existing nodes. According to the principles of FTSeq construction, it is required that at least one endpoint of $e_{i+1}$ connects to existing atoms, namely the left atom $e_{i+1}^l$ . Given the set of previously generated atoms $\{v_1, v_2, \cdots, v_j\}$ and their corresponding graph position encodings $\mathbf{O}_j = [o_{v_1}, o_{v_2}, \cdots, o_{v_j}] \in \mathbb{R}^{j,C}$ in $\mathcal{B}'_p$ , we predict the position encoding of the left node using a linear layer $\operatorname{PredPos}^l(\cdot)$ :
+
+$$\hat{\boldsymbol{g}}_{e_{i+1}}^{l} = \operatorname{PredPos}^{l}(\boldsymbol{h}_{e_{i+1}}) \in \mathbb{R}^{1,C}. \tag{7}$$
+
+We compute the cosine similarity between $\hat{g}_{e_{i+1}}^l$ and $\mathbf{O}_j$ by $c^l = \hat{g}_{e_{i+1}}^l \mathbf{O}_j^T \in \mathbb{R}^t$ . The index of existing atoms that $e_{i+1}^l$ will attach to is $u_l = \arg\max c^l$ . This process implicitly infers edge connections by querying over existing atoms, instead of generating all potential edges from scratch. We update the graph position encoding of the left node as:
+
+$$\boldsymbol{g}_{e_{i+1}}^l = \boldsymbol{o}_{v_{u_l}}$$
+ Left Node GPE. (8)
+
+**Step 3: Right Node Placement.** As for the right node $e_{i+1}^r$ , we consider two cases: (1) it connects to one of the existing atoms; (2) it is a new atom. Similar to the step 2, we use a linear layer $\operatorname{PredPos}^r(\cdot)$ to predict the position encoding of the right node:
+
+$$\hat{\boldsymbol{g}}_{e_{i+1}}^r = \operatorname{PredPos}^r(\boldsymbol{h}_{e_{i+1}}) \in \mathbb{R}^{1,C}.$$
+ (9)
+
+We get the cosine similarity score $\mathbf{c}^r = \hat{\mathbf{g}}_{e_{i+1}}^T \mathbf{O}_j^T$ and the index of node with the highest similarity $u_r = \arg\max \mathbf{c}^r$ . Given a predefined threshold $\epsilon$ , if $\mathbf{c}_k > \epsilon$ , we consider $e_{i+1}$ is connected to $v_{u_r}$ , and update:
+
+$$\boldsymbol{g}_{e_{i+1}}^r = \boldsymbol{o}_{v_{u_r}}$$
+ Right Node GPE, Case 1; (10)
+
+otherwise, $e_{i+1}^r$ is a new atom $v_{j+1}$ , and we set:
+
+$$\boldsymbol{g}_{e_{i+1}}^r = \boldsymbol{o}_{j+1}$$
+ Right Node GPE, Case 2. (11)
+
+Finally, we update the FTSeq by:
+
+$$\begin{cases} \texttt{FTSeq} \leftarrow [\texttt{FTSeq}, e_{i+1}] & \texttt{Case 1} \\ \texttt{FTSeq} \leftarrow [\texttt{FTSeq}, e_{i+1}, v_{j+1}] & \texttt{Case 2} \end{cases} . \tag{12}$$
+
+By default, we set $\epsilon = 0.5$ .
+
+**Training Node Attachment & Placement.** We adopt a contrastive objective to optimize left node attachment and right node placement problems. Taking left node attachment as an example, given the ground truth t, i.e., the index of the attached atom in the original graph, the positive score is $s^+ = e^l_{i+1} o^T_{v_t}$ , while the negative scores are $s^- = |\text{vec}(\mathbf{OO}^T)| \in \mathbb{R}^{|\mathcal{B}'_p| \times (|\mathcal{B}'_p| - 1)}$ , where $\text{vec}(\cdot)$ is a flatten operation while ignoring the diagonal elements. The final contrastive loss is:
+
+$$\mathcal{L}_{\text{attach}} = (1 - s^{+}) + \frac{1}{|\mathcal{B}'_{p}| \times (|\mathcal{B}'_{p}| - 1)} \sum s^{-}.$$
+ (13)
+
+Block-Wise Causal Attention. In our method, node generation is closely entangled with edge generation. Specifically, on its initial occurrence, each node is connected to an edge, creating what we term a block. From the block view, we employ a causal mask for auto-regressive generation. However, within each block, we utilize the full attention. We show the block-wise causal attention in Figure 4.
+
+
+
+Figure 4: Block-Wise causal attention with grey cells indicating masked positions. Graph Words contribute to the generation through full attention, serving as prefix prompts.
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+# Introduction
+
+Large Language Models (LLMs) such as OpenAI's GPT [@brown2020language] and Llama 2 [@touvron2023llama] have been highly effective across a diverse range of language understanding and reasoning tasks [@liang2023holistic]. While LLM performances have been thoroughly investigated across various benchmarks [@superglue; @big-bench; @eval-harness; @touvron2023llama], the principles and properties behind their step-wise reasoning process are still poorly understood [@valentino-etal-2021-natural]. LLMs are notoriously black-box models and can be difficult to interpret [@dl-interp-survey; @danilevsky-etal-2020-survey]. Moreover, the commercialization of LLMs has led to strategic secrecy around model architectures and training details [@vice; @wired]. Finally, neural models are susceptible to hallucinations and adversarial perturbations [@geirhos2020shortcut; @camburu2020make], often producing plausible but factually incorrect answers [@survey-hallucinations; @huang2023survey]. As the size and complexity of LLM architectures increase, the systematic study of generated explanations becomes crucial to better interpret and validate the LLM's internal inference and reasoning processes [@wei2022chain; @lampinen2022can; @huang2022towards].
+
+The automatic evaluation of natural language explanations presents several challenges [@atanasova-etal-2023-faithfulness; @camburu2020make]. Without resource-intensive annotation [@wiegreffe2021teach; @thayaparan2020survey; @dalvi2021explaining; @camburu2018snli], explanation quality methods tend to rely on either weak supervision, where the identification of the correct answer is taken as evidence of explanation quality, or require the injection of domain-specific knowledge [@quan2024enhancing]. In this paper, we seek to better understand the LLM explanatory process through the investigation of explicit linguistic and logical properties. While explanations are hard to formalize due to their open-ended nature, we hypothesize that they can be analyzed as linguistic objects, with measurable features that can serve to define criteria for assessing their quality.
+
+Specifically, this paper investigates the following overarching research question: *"Can linguistic and logical properties associated with LLMs' generated explanations be used to qualify the models' reasoning process?"*. To this end, we propose an interpretable framework inspired by philosophical accounts of abductive inference, also known as *Inference to the Best Explanation (IBE)* - i.e. the process of selecting among competing explanatory theories [@lipton2017inference]. In particular, we aim to measure the extent to which LLM-generated explanations conform to IBE expectations when attempting to identify the most plausible explanation. To this end, we present *IBE-Eval*, a framework designed to estimate the plausibility of natural language explanations through a set of explicit logical and linguistic features, namely: *logical consistency*, *parsimony*, *coherence*, and *linguistic uncertainty*.
+
+To evaluate the efficacy of *IBE-Eval*, we conduct extensive experiments in the multiple-choice Causal Question Answering (CQA) setting. The overall results and contributions of the paper can be summarized as follows:
+
+1. To the best of our knowledge, we are the first to propose an interpretable framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to automatically assess the quality of natural language explanations.
+
+2. We propose *IBE-Eval*, a framework that can be instantiated with external tools for the automatic evaluation of LLM-generated explanations and the identification of the best explanation in the multiple-choice CQA settings.
+
+3. We provide empirical evidence that LLM-generated explanations tend to conform to IBE expectations with varying levels of statistical significance correlated to the LLM's size.
+
+4. We additionally find that uncertainty, parsimony, and coherence are the best predictors of plausibility and explanation quality across all LLMs. However, we also find that the LLMs tend to be strong rationalizers and can produce consistent explanations even for less plausible candidates, making the consistency metric less effective in practice.
+
+5. *IBE-Eval* can successfully identify the best explanation supporting the correct answers with up to 77% accuracy (+$\approx 27\%$ above random and +$\approx17\%$ over GPT 3.5-as-a-Judge baselines)
+
+6. IBE-Eval method is significantly correlated with human judgment, outperforming a GPT3.5-as-a-Judge baseline in terms of alignment with human preferences.
+
+For reproducibility, the entire experimental code and analysed explanations are available online[^1] to encourage future research in the field.
+
+Explanatory reasoning is a distinctive feature of human rationality underpinning problem-solving and knowledge creation in both science and everyday scenarios [@lombrozo2012explanation; @deutsch2011beginning]. Accepted epistemological accounts characterize the creation of an explanation as composed of two distinct phases: conjecturing and criticism [@popper2014conjectures]. The explanatory process always involves a conflict between plausible explanations, which is typically resolved through the criticism phase via a selection process, where competing explanations are assessed according to a set of criteria such as parsimony, coherence, unification power, and hardness to variation [@lipton2017inference; @harman1965inference; @mackonis2013inference; @thagard1978best; @thagard1989explanatory; @kitcher1989explanatory; @valentino2022scientific].
+
+As LLMs become interfaces for natural language explanations, epistemological frameworks offer an opportunity for developing criticism mechanisms to understand the explanatory process underlying state-of-the-art models. To this end, this paper considers an LLM as a conjecture device producing linguistic objects that can be subject to criticism. In particular, we focus on a subset of criteria that can be computed on explicit linguistic and logical features, namely: *consistency*, *parsimony*, *coherence*, and *uncertainty*.
+
+To assess the LLM's alignment to such criteria, we focus on the task of selecting among competing explanations in a multiple-choice CQA setting (Figure [1](#fig:overall_framework){reference-type="ref" reference="fig:overall_framework"}). Specifically, given a set of competing hypotheses (i.e. the multiple-choice options), $H = \{h_1, h_2, \ldots, h_n\}$, we prompt the LLM to generate plausible explanations supporting each hypothesis (Section [3](#sec:prompting){reference-type="ref" reference="sec:prompting"}). Subsequently, we adopt the proposed IBE selection criteria to assess the quality of the generated explanations (Section [\[sec:explanation_criteria\]](#sec:explanation_criteria){reference-type="ref" reference="sec:explanation_criteria"}). *IBE-Eval* computes an explanation score derived from the linear combination of the computed selection criteria. The explanation with the highest score is selected as the predicted answer and additionally assessed as to the extent to which the observable IBE features are correlated with QA accuracy. We hypothesize that IBE-Eval will produce higher scores for the explanation associated with the correct answer and that IBE criteria should meaningfully differentiate between competing explanations.
+
+For the first stage, the LLM is prompted to generate competing explanations for the hypotheses using a modified Chain-of-Thought (CoT) prompt [@cot-prompting]. Specifically, the COT prompt is modified to instruct the LLM to produce an explanation for each competing hypothesis (see Figure [1](#fig:overall_framework){reference-type="ref" reference="fig:overall_framework"}). We adopt a methodology similar to @valentino-etal-2021-natural, where the generated explanation is constrained into an entailment form for the downstream IBE evaluation. In particular, we posit that a valid explanation should demonstrate an entailment relationship between the premise and conclusion which are derived from the question-answer pair.
+
+To elicit logical connections between explanation steps and facilitate subsequent analysis, the LLM is constrained to use weak syllogisms expressed as If-Then statements. Additionally, the LLM is instructed to produce the associated causal or commonsense assumption underlying each explanation step. This output is then post-processed to extract the explanation steps and supporting knowledge for evaluation via the IBE selection criteria. Additional details and examples of prompts are reported in Appendix [12.2](#sec:appendix-prompting){reference-type="ref" reference="sec:appendix-prompting"}.
+
+To perform IBE, we investigate a set of criteria that can be automatically computed on explicit logical and linguistic features, namely: *consistency*, *parsimony*, *coherence*, and *uncertainty*.
+
+[]{#sec:explanation_criteria label="sec:explanation_criteria"}
+
+Consistency aims to verify whether the explanation is logically valid. Given a hypothesis, composed of a premise $p_i$, a conclusion $c_i$, and an explanation consisting of a set of statements $E = {s_1, ..., s_i}$, we define $E$ to be logically consistent if $p_i \cup E \vDash c_i$. Specifically, an explanation is logically consistent if it is possible to build a deductive proof linking premise and conclusion. To evaluate logical consistency, we leverage external symbolic solvers along with autoformalization - i.e., the translation of natural language into a formal language [@wu2022autoformalization]. Specifically, hypotheses and explanations are formalized into a Prolog program which will attempt to generate a deductive proof via backward chaining [@weber2019nlprolog]. To perform autoformalization, we leverage the translation capabilities of GPT 3.5. Specifically, we instruct GPT 3.5 to convert each IF-Then implication from the generated explanation into an implication rule and the premise statement into grounding atoms. On the other end, the entailment condition and the conclusion are used to create a Prolog query. Further details about the autoformalization process can be found in Appendix [12.3](#sec:appendix-autoformalization){reference-type="ref" reference="sec:appendix-autoformalization"}. After autoformalization, following recent work on neuro-symbolic integration for LLM explanations [@quan2024enhancing], we adopt an external Prolog solver for entailment verification[^2]. The explanation is considered consistent if the Prolog solver can satisfy the query and successfully build a deductive proof. Technical details can be found in Appendix [12.6](#sec:appendix-consistency){reference-type="ref" reference="sec:appendix-consistency"}.
+
+The parsimony principle, also known as Ockham's razor, favors the selection of the simplest explanation consisting of the fewest elements and assumptions [@sober-parsimony]. Epistemological accounts posit that an explanation with fewer assumptions tends to leave fewer statements unexplained, improving specificity and alleviating the infinite regress [@thagard1978best]. Further, parsimony is an essential feature of causal interpretability, as only parsimonious solutions are guaranteed to reflect causation in comparative analysis [@baumgartner2015parsimony]. In this paper, we adopt two metrics as a proxy of parsimony, namely *proof depth*, and *concept drift*. Proof depth, denoted as $Depth$, is defined as the cardinality of the set of rules, $R$, required by the Prolog solver to connect the conclusion to the premise via backward chaining. Let $h$ be a hypothesis candidate composed of a premise $p$ and a conclusion $c$, and let $E$ be a formalized explanation represented as a set of rules $R'$. The proof depth is the number of rules $|R|$, with $R \subseteq R'$, traversed during backward chaining to connect the conclusion $c$ to the premise $p$:
+
+$$Depth(h) = |R|$$ Concept drift, denoted as $Drift$, is defined as the number of additional concepts and entities, outside the ones appearing in the hypothesis (i.e., premise and conclusion), that are introduced by the LLM to support the entailment. For simplicity, we consider nouns as concepts. Let $N = \{Noun_{p}, Noun_{c}, Noun_{E} \}$ be the unique nouns found in the premise, conclusion, and explanation steps. Concept drift is the cardinality of the set difference between the nouns found in the explanation and the nouns in the hypothesis: $$Drift(h) = |Noun_{E} - (Noun_{p} \cup Noun_{c})|$$ Intuitively, the parsimony principle would predict the most plausible hypothesis as the one supported by an explanation with the smallest observed proof depth and concept drift. Implementation details can be found in Appendix [12.7](#sec: appendix-parsimony){reference-type="ref" reference="sec: appendix-parsimony"}.
+
+*Coherence* attempts to measure the logical relations within individual explanatory statements and implications. An explanation can be formally consistent on the surface while still including implausible or ungrounded intermediate assumptions. Coherence evaluates the quality of each intermediate If-Then implication by measuring the entailment strength between the If and Then clauses. To this end, we employ a fine-tuned natural language inference (NLI) model. Formally, let $S$ be a set of explanation steps, where each step $s$ consists of an If-Then statement, $s = (If_s, Then_s)$. For a given step $s_i$, let $ES(s_i)$ denote the entailment score obtained via the NLI model between $If_s$ and $Then_s$ clauses. The step-wise entailment score $SWE(S)$ is then calculated as the averaged sum of the entailment scores across all explanation steps $|S|$: $$\text{SWE}(S) = \frac{1}{|S|}\sum_{i=1}^{|S|} \text{ES}(s_i)$$ We hypothesize that the LLM should generate a higher coherence score for more plausible hypotheses, as such explanations should exhibit stronger step-wise entailment. Additional details can be found in Appendix [12.8](#sec: appendix-coherence ){reference-type="ref" reference="sec: appendix-coherence "}.
+
+Finally, we consider the linguistic certainty expressed in the generated explanation as a proxy for plausibility. Hedging words such as *probably*, *might be*, *could be*, etc typically signal ambiguity and are often used when the truth condition of a statement is unknown or improbable. @pei-jurgens-2021-measuring found that the strength of scientific claims in research papers is strongly correlated with the use of direct language. In contrast, they found that the use of hedging language suggested that the veracity of the claim was weaker or highly contextualized. To measure the linguistic certainty ($LC$) of an explanation, we consider the explanation's underlying assumptions ($A_i$) and the overall explanation summary ($S$). The linguistic certainty score is extracted using the fine-tuned sentence-level RoBERTa model from @pei-jurgens-2021-measuring. The overall linguistic certainty score ($LC_{\text{overall}}$) is the sum of the assumption and explanation summary scores:
+
+$$LC_{\text{overall}} = LC(A) + LC(S)$$ Where $LC(A)$ is the sum of the linguistic certainty scores ($LC(A)$) across all the assumptions $|A|$ associated with each explanation step $i$:
+
+$$LC(A) = \sum_{i=1}^{|A|} LC(a_i)$$ and linguistic certainty of the explanation summary $LC(S)$. We hypothesize that the LLM will use more hedging language when explaining the weaker hypothesis resulting in a higher uncertainty score. Further details can be found in Appendix [12.9](#sec: appendix-uncertainty){reference-type="ref" reference="sec: appendix-uncertainty"}.
+
+After the IBE criteria are computed for each competing hypothesis, they are used to generate the final explanation score. We define a simple linear regression model $\theta(\cdot)$, which was fitted on a small set of training examples consisting of extracted IBE features to predict the probability that an explanation $E_i$ corresponds to the correct answer. Specifically, we employ *IBE-Eval* to score each generated explanation independently and then select the final answer $a$ via argmax:
+
+$$a = \mathop{\mathrm{argmax}}_i [\theta(E_1),\ldots, \theta(E_n)]$$
+
+Additional details can be found in Appendix [12.10](#sec: appendix-ibe){reference-type="ref" reference="sec: appendix-ibe"}.
+
+
+
+ Regression analysis measuring the correlation between IBE criteria and question accuracy. All LLMs tend to conform to IBE expectations with GPT 3.5 exhibiting the most consistent and significant alignment. Linguistic uncertainty is the strongest IBE predictor for explanation quality, where higher uncertainty is negatively correlated with question accuracy. Statistical significance is noted as: ‘***’ p < 0.001, ‘**’ p < 0.01 ‘*’ p < 0.05.
+
+
+::: table*
++----------------------------------+------------------------------------------------+------------------------------------------------+
+| | **COPA** | **E-CARE** |
++:=================================+:===========:+:===============:+:==============:+:===========:+:===============:+:==============:+
+| | **GPT 3.5** | **LlaMA 2 13B** | **LlaMA 2 7B** | **GPT 3.5** | **LlaMA 2 13B** | **LlaMA 2 7B** |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| [**Baselines**]{.underline} | | | | | | |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| GPT3.5 Judge | .59 | .47 | .63 | .43 | .61 | .52 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| ***Human*** | ***.95*** | ***1.0*** | ***.91*** | ***.90*** | ***.91*** | ***.92*** |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| [**IBE Features**]{.underline} | | | | | | |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Consistency | .51 | .52 | .55 | .54 | .54 | .54 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Depth (Parsimony) | .67 | .53 | .63 | .66 | .56 | .54 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Drift (Parsimony) | .67 | .63 | .58 | .66 | .57 | .57 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Coherence | .66 | .66 | .56 | .56 | .57 | .59 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Linguistic Uncertainty | .70 | .65 | .61 | .59 | .56 | .60 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| [**Composed Model**]{.underline} | | | | | | |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| Random | .50 | .50 | .50 | .50 | .50 | .50 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| \+ Consistency | .51 | .52 | .55 | .54 | .54 | .54 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| \+ Depth | .67 | .53 | .63 | .66 | .56 | .56 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| \+ Drift | .70 | .65 | .65 | .72 | .66 | .65 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| \+ Coherence | .73 | .71 | .69 | .73 | .68 | .69 |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+| \+ Linguistic Uncertainty | **.77** | **.74** | **.70** | **.74** | **.70** | **.73** |
++----------------------------------+-------------+-----------------+----------------+-------------+-----------------+----------------+
+:::
+
+# Method
+
+We present a preliminary sanity-check measuring the extent to which LLMs generate *self-evident or tautological* explanations - i.e., explanations that simply restate the premises and conclusions. Tautological explanations represent a risk for IBE-Eval as the metrics would be theoretically uninformative if the LLM adopts the tested causal relation as the explanation itself (e.g. A → B) without providing additional supporting statements.
+
+We consider the *parsimony* metric to compute the percentage of explanations with *proof depth* equal to 1 (i.e, explanations containing only one inference step) and *concept drift* equal to 0 (i.e. no additional concepts other than the ones stated in premises and conclusions appear in the explanation). In such cases, the LLM is effectively generating a self-evident or tautological explanation.
+
+**We found that about 2% of the cases consist of self-evident explanations**. For GPT 3.5, Llama 2 13B and 7B, 2% of the generated explanations exhibit a concept drift of 0, and on average 1.5% of the explanations have a proof depth of 1. Next, we conduct an error analysis to evaluate the cases where *IBE-Eval* selected a self-evident explanation as the best one. **Across all LLMs, less than 0.1% of the errors were caused by the selection of such explanations**. Our analysis suggests that the impact of self-evident explanations is not significant and that the IBE framework can be robustly applied to identify such cases.
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diff --git a/2404.09571/paper_text/intro_method.md b/2404.09571/paper_text/intro_method.md
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+# Introduction
+
+Image super-resolution (ISR) is an important research topic in image processing; its purpose is to create a high-resolution (HR) image with improved perceptual quality and richer spatial detail from a corresponding low-resolution (LR) version. It is widely used in applications including medical imaging [@wang2020deep], image restoration [@liang2021swinir], enhancement [@singh2014various], and picture coding [@afonso2018video]. The past decade has seen impressive performance improvements due to extensive research in this area, in particular associated with advances in deep learning techniques. Learning-based ISR approaches can be classified according to their basic network structure [@aleissaee2023transformers]. One major class is based on convolutional neural networks (CNNs), with notable examples including *e.g.*, SRCNN [@dong2015image], EDSR [@lim2017enhanced] and RCAN [@zhang2018image; @lin2022revisiting]. A second class employs Vision Transformer (ViT) networks with important contributions such as SwinIR [@liang2021swinir], Swin2SR [@conde2022swin2sr] and HAT [@chen2023hat].
+
+Although these learning-based ISR algorithms have demonstrated superior performance over conventional methods based on classic signal processing theory, they are typically associated with high computational complexity and memory requirements, often inhibiting their practical deployment. To address this issue, research has focused on the development of lightweight ISR methods for real-world applications [@jiang2023compressing; @cai2022real]. These works often obtain compact models through model compression [@buciluǎ2006model] or other simplification [@liang2021swinir] of their corresponding full versions. However, when these lite ISR models are directly optimized based on the same training material used for their original counterparts, they generally achieve lower super-resolution performance due to their reduced model capacity and limited learning ability.
+
+
+
+Illustration of the proposed Multi-Teacher Knowledge Distillation framework.
+
+
+To further improve the performance of these low complexity learning-based ISR methods, knowledge distillation (KD) techniques [@hinton2015distilling] have been commonly applied. These employ a larger network as a "teacher" that transfers knowledge to a smaller "student" network. In this process, an auxiliary loss function is often employed to instruct the student to mimic the teacher's output, through minimizing the disparity between intermediate features or by aligning their final predictions. This type of approach has demonstrated promising outcomes for various applications such as image classification [@jin2023multi; @miles2023understanding_AAAI], video compression [@peng2023accelerating], object detection [@shi2020proxylesskd; @zhu2023scalekd], natural language processing (NLP) [@jiao2019tinybert; @liu2023pre; @kang2023distill] and quality assessment [@feng2023rankdvqa].
+
+Many prior studies [@fang2022cross; @zhang2023data; @he2020fakd; @morris2023st] in KD predominantly focus on the transfer of knowledge from a single teacher to its corresponding student. This strategy loses the advantages of well-performing complex models. They also employ simple loss objectives, such as L1, to minimize the output difference between the teacher and student models; this does not fully reflect the nature of image super-resolution in reconstructing lost high-frequency information in the higher-resolution content.
+
+In this context, inspired by the advances in multi-teacher selection based KD for natural language processing tasks [@yuan2021reinforced] and the classic wavelet transforms [@heil1989continuous], this paper presents a novel Multi-Teacher Knowledge Distillation (MTKD) framework for image super-resolution based on a new wavelet-inspired loss function. As illustrated in Fig. [1](#fig:multiKDfw){reference-type="ref" reference="fig:multiKDfw"}, MTKD employs a new Discrete Cosine Transform Swin transformer (DCTSwin) based network to combine the outputs of multiple ISR teacher models and generates an enhanced representation of the high-resolution image, which is then used to guide the student model during distillation. For improving knowledge transfer performance, we have designed a new distillation loss function based on the discrete wavelet transform, which compares the output of the student and the enhanced representation within different frequency subbands. The primary contributions of this work are summarized as follows.
+
+- We, for the first time, propose a novel Multi-Teacher Knowledge Distillation (MTKD) framework for image super-resolution. This framework allows the use of multiple teacher models with different network architectures, which improves the efficiency and diversity of transferred knowledge.
+
+- We have developed a knowledge aggregation network based on novel DCTSwin blocks, which is employed to combine the outputs of multiple teachers to produce a refined representation for ISR knowledge distillation.
+
+- We have designed an ISR-specific and wavelet-based loss function which collects information from different frequency subbands allowing the model to effectively learn high-frequency information from the original images, which can enhance the performance of knowledge distillation for image super-resolution.
+
+We have demonstrated the superior performance of the proposed method through quantitative and qualitative evaluations based on three teacher and student models with distinct network architectures. Our MTKD approach has been compared against five existing KD methods and achieved consistent and evident performance gains, up to 0.46dB assessed by PSNR.
+
+# Method
+
+As illustrated in Fig. [1](#fig:multiKDfw){reference-type="ref" reference="fig:multiKDfw"}, our Multi-Teacher Knowledge Distillation (MTKD) framework consists of two primary stages: 1) knowledge aggregation and 2) model distillation. In Stage 1, the input low-resolution image $I_\mathrm{LR}\in \mathbb{R}^{H \times W \times C_\mathrm{in}}$ ($H$, $W$ and $C_\mathrm{in}$ represent the image height, width and the number of color channels respectively) is reconstructed by $N$ pre-trained ISR teacher models, denoted by ${M}^1_T \dots M^N_{T}$, resulting in $N$ high-resolution images, $I^{1}_\mathrm{HR} \dots I^{N}_\mathrm{HR}$. These are then concatenated and fed into a Knowledge Aggregation module, which outputs an enhanced high-resolution image, $I^\mathrm{MT}_\mathrm{HR}$. Once the Knowledge Aggregation module is optimized, its parameters are then fixed in Stage 2 when the student model is trained through knowledge distillation.
+
+The architecture of the network employed for knowledge aggregation is shown in Fig. [1](#fig:multiKDfw){reference-type="ref" reference="fig:multiKDfw"}. The input of the network is the concatenation of $N$ teachers' outputs, $[I^{1}_\mathrm{HR} \dots I^{N}_\mathrm{HR}] \in \mathbb{R}^{sH \times sW \times C_\mathrm{in} \times N}$, in which $s$ is the up-scale factor in ISR. A pixel unshuffle layer (together with an additional $3 \times 3$ convolution layer) is then used to down-sample the spatial resolution of the input by a factor of $s$. The down-sampling factor here is the same as the super-resolution up-scale rate in order to keep the following DCTSwin blocks independent from the upscale factor. The feature set output by the convolutional layer is denoted by $F_{s} \in \mathbb{R}^{H \times W \times C}$, where $C$ represents the feature channel number that is a fixed hyperparameter. $F_{s}$ are then processed by $B$ DCTSwin blocks, generating deep features $F^{1}_{d}, F^{2}_{d} \dots F^{B}_{d}$. The last set of features $F^{B}_{d}$ is fed into a $3 \times 3$ convolutional layer to produce $F_{d}$, which will be combined with the shallow features $F_{s}$ before being upsampled to the full image resolution. The upsample module is implemented based on the sub-pixel convolution layer [@shi2016real].
+
+As shown in Fig. [1](#fig:multiKDfw){reference-type="ref" reference="fig:multiKDfw"}, each DCTSwin block contains $L$ DCTSwin Transformer Layers (DCTSTLs), which are modified from the original Swin Transformer layer [@liu2021swin]. It first normalizes the input using a Layer Norm before performing Discrete Cosine Transform (DCT). The transformed coefficients are then processed by the Shifted Window Multi-head Self-Attention (SW-MSA) module [@liu2021swin]. The output of the SW-MSA module is further fed into the Inverse DCT (IDCT) module to recover the features in the spatial domain, which are combined with the input of this DCTSTL to achieve residual learning. Here to reduce the number of model parameters and facilitate fast DCT operation, the input of the DCT is segmented into $W_s\times W_s$ blocks prior to the SW-MSA module through window partition [@liu2021swin], and the window reverse operation is performed after the IDCT module to reshape and assemble the output.
+
+To train the Knowledge Aggregation module, we optimize its network parameters by minimizing the L1 loss between the ground-truth image, $I_{GT}$, and the output of the module, $I^\mathrm{MT}_\mathrm{HR}$: $$\begin{equation}
+ \mathcal{L}_\mathrm{KA} = \left \| I_\mathrm{GT} - I^\mathrm{MT}_\mathrm{HR} \right \|_{1}.
+\end{equation}$$
+
+In Stage 2, the network parameters of the optimized Knowledge Aggregation module will be fixed for student-teacher distillation. Here our new MTKD approach does not require the student network to have a similar architecture to one of the teacher models. This allows us to employ teacher models with diverse network architectures in order to achieve improved knowledge distillation performance.
+
+The output of the Knowledge Aggregation module and the ground-truth image are jointly employed to train the student model. Specifically, we first compare the output of the student model $I_\mathrm{stu}$ with the ground truth based on L1 loss: $$\begin{equation}
+ \mathcal{L}_{stu} = \left \| I_\mathrm{stu} - I_\mathrm{GT} \right \|_{1}.
+\end{equation}$$ The design of the distillation loss is inspired by [@zhang2022wavelet], where a wavelet-based training methodology was employed for image-to-image translation. It is calculated between $I_\mathrm{stu}$ and $I^\mathrm{MT}_\mathrm{HR}$ after decomposing both using a discrete wavelet transform (DWT): $$\begin{align}
+ \mathcal{L}_{dis} & = \frac{1}{3K+1} \sum_{i,k} \left \| \mathrm{DWT}_{i,k}(I_\mathrm{stu}) - \mathrm{DWT}_{i,k}(I^\mathrm{MT}_\mathrm{HR}) \right \|_{1},
+\end{align}$$ in which $i\in \{\mathrm{LL, LH, HL, HH}\}$. Here $k=1 \dots K$, which stands for the DWT decomposition level.
+
+These two losses are then combined as the overall loss function $\mathcal{L}_{total}$ as given below. $$\begin{align}
+ \mathcal{L}_{total} & = \alpha\mathcal{L}_{stu}(I_\mathrm{stu}, I_\mathrm{GT}) + \mathcal{L}_{dis}(I_{stu}, I^\mathrm{MT}_\mathrm{HR}),
+\end{align}$$ where $\alpha$ is a tunable weight to determine the contributions of two losses. It is noted here that we did not employ a wavelet-based loss for $\mathcal{L}_{stu}$. This is because (i) L1 loss is the most commonly used loss function to minimize the difference between the model output and the ground truth; (ii) due to the small $\alpha$ value used, the contribution of $\mathcal{L}_{stu}$ is rather limited. Using L1 loss here can effectively reduce the training complexity without compromising the performance.
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diff --git a/2407.01163/main_diagram/main_diagram.pdf b/2407.01163/main_diagram/main_diagram.pdf
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diff --git a/2407.01163/paper_text/intro_method.md b/2407.01163/paper_text/intro_method.md
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+# Introduction
+
+In 1999, @rao1999predictive ([-@rao1999predictive]) proposed a formulation of predictive coding (PC) to model hierarchical information processing in the brain. It was recently realized that this framework could be used to train neural networks using a bio-plausible learning rule [@whittington2017approximation]. This has led to different research directions, whose focus was either to explore interesting properties of PC networks [@song2024inferring; @alonso2022theoretical], or to propose variations that improve the performance on specific tasks [@salvatori2022incremental; @ororbia2022neural]. These lines of research, however, have the tendency of not comparing their results against other works, and to focus on small-scale experiments. The field is hence avoiding what we believe to be the most important open problem: scalability.
+
+There are multiple reasons why the problem of scalability has been overlooked. First, it is a hard problem, and it is still unclear why so far PC has been able to perform as well as classical gradient descent with backpropagation (BP) only up to a certain scale, which is that of small convolutional models trained to classify the CIFAR10 dataset [@salvatori2022incremental]. Understanding this would allow us to develop regularization techniques that stabilize learning, and hence allow better performance on more complex tasks. Second, the lack of specialized libraries makes PC models extremely slow: a full hyperparameter search on a small convolutional network can take several hours. Third, the lack of a common framework makes reproducibility and iterative contributions hard, as implementation details or code are rarely provided. In this work, we make the first steps toward addressing these problems with three contributions, that we call *tool*, *benchmarking*, and *analysis*.
+
+We release an open-source library for accelerated training for predictive coding called PCX. This library runs in JAX [@jax2018github], and offers a user-friendly interface with a minimal learning curve through familiar syntax inspired by Pytorch. We also provide extensive tutorials. It is also fully compatible with Equinox [@kidger2021equinox], a popular deep-learning-oriented extension of JAX, ensuring reliability, extendability, and compatibility with ongoing research developments. It also supports JAX's Just-In-Time (JIT) compilation, making it efficient and allowing both easy development and execution of PC networks, gaining efficiency with respect to existing libraries.
+
+We propose a uniform set of tasks, datasets, metrics, and architectures that should be used as a skeleton to test the performance of future variations of PC. The tasks that we propose are the standard ones in computer vision: image classification and generation. The models that we use, as well as the datasets, are picked according to two criteria: First, to allow researchers to test their algorithm from the easiest task (feedforward network on MNIST) to more complex ones; Second, to favor the comparison against related fields in the literature, such as equilibrium and target propagation [@scellier17; @bengio2014auto]. To this end, we have picked some of the models that are consistently used in their research papers. As learning algorithms, we consider standard PC, incremental PC [@salvatori2022incremental], PC with Langevin dynamics [@oliviers2024learning], and nudged PC, as done in the Eqprop literature [@scellier17; @scellier2024energy]. Note that this is the first time nudging algorithms are applied in PC models.
+
+We get state-of-the-art (SOTA) results for PC on multiple benchmarks and show for the first time that it is able to perform well on more complex datasets, such as CIFAR100 and Tiny Imagenet, where we get results comparable to those of backprop. In image generation tasks, we present experiments on datasets of colored images, going beyond MNIST and FashionMNIST as performed in previous works. We thoroughly discuss the results and highlight areas of improvement, the main one being generalization to very deep models, and report analysis on the credit assignment of PC in such cases, to better understand the reasons behind some failures. To conclude, in the supplementary material we provide a detailed explanation of hyperparameters/techniques/tricks that allowed us to reach SOTA results, to also provide a *cookbook* for researchers in the field.
+
+# Method
+
+We consider various learning algorithms present in the literature: (1) Standard PC, already discussed in the background section; (2) Incremental PC (iPC) [@salvatori2022incremental], a simple and recently proposed modification where the weight parameters are updated alongside the latent variables at every time step; (3) Monte Carlo PC (MCPC) [@oliviers2024learning], obtained by applying unadjusted Langevin dynamics to the inference process; (4) Positive nudging (PN), where the target used is obtained by a small perturbation of the output towards the original, 1-hot label; (5) Negative nudging (NN), where the target is obtained by a small perturbation *away* from the target, and updating the weights in the opposite direction; (6) Centered nudging (CN), where we alternate epochs of positive and negative nudging [@scellier2024energy]. Among these, PC, iPC, and MCPC will be used for the generative mode, and PC, iPC, PN, NN, and CN for the discriminative mode. See Fig. [1](#fig:exp_1){reference-type="ref" reference="fig:exp_1"}, and the supplementary material, for a more detailed description.
+
+::: table*
+[]{#tab:accuracy label="tab:accuracy"}
+:::
+
+We test the performance of PCNs on image classification tasks by comparing PC against BP, using both *Squared Error* (SE) and *Cross Entropy* (CE) loss, by adapting the energy function as described in [@pinchetti2022]. For the experiments on MNIST and FashionMNIST, we use feedforward models with $3$ hidden layers of $128$ hidden neurons, while for CIFAR10/100 and Tiny ImageNET, we compare ResNets and VGG-like models [@he2016deep; @vgg].
diff --git a/2409.17754/main_diagram/main_diagram.drawio b/2409.17754/main_diagram/main_diagram.drawio
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diff --git a/2409.17754/paper_text/intro_method.md b/2409.17754/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..3237eb39d6a1850717638f7b2e40d6c5b8e29886
--- /dev/null
+++ b/2409.17754/paper_text/intro_method.md
@@ -0,0 +1,145 @@
+# Introduction
+
+The integration of Machine Learning (ML) into modern technology has significantly transformed various sectors, particularly with the proliferation of Internet of Things (IoT) devices [@Mbock20]. This growth has led to an exponential increase in the generation and distribution of big data across devices, highlighting both new opportunities and challenges for advanced data processing techniques [@Suganyadevi22]. The classical approach to handling this amount of data has several disadvantages in terms of security, privacy, and scalability, as all this data (and the trained algorithm) is stored and processed in a large centralized datacenter [@Shokri15; @Liu20]. In response to these challenges, Federated Learning (FL) has been proposed as an alternative paradigm that allows machine learning algorithms to work without the centralization of data [@McMahan17]. FL leverages the computational capabilities of end devices, enabling them to train their own ML models with their local data. A central server then merges all the locally trained models from the end devices to compute a global model [@Bonawitz17]. This method enhances privacy by design, as it does not expose all data at a single location, while utilizing the processing capabilities of the devices. However, traditional federated learning still relies on a central server to manage the learning algorithm process, which can be a target for privacy and security attacks by malicious attackers [@Truex19], among other ongoing challenges.
+
+The concept of Decentralized Federated Learning (DFL) extends FL principles by eliminating the central server, thereby mitigating the risks associated with a single point of failure and enhancing privacy by avoiding a centralized coordination of the learning process [@Yuan24]. This decentralized approach promotes greater resilience, allowing the learning process to continue even if some nodes fail or go offline, thus improving the robustness and fault tolerance of the overall network [@Li22].
+
+However, while the decentralization of these learning environments offers benefits such as improved scalability, it also introduces new challenges, including increased vulnerability to security threats. The absence of a central server complicates the implementation of uniform security-preserving algorithms and makes the system more susceptible to attacks such as Byzantine failures [@Lyu20; @Fang21]. Notably, Byzantine failures occur when a group of Byzantine clients attempts to disrupt the proper learning process, e.g., by altering their updates to degrade other local model's performance (known as Byzantine attacks [@Marano09]). Additionally, the heterogeneity among nodes, with varying computational capabilities or data quality, can lead to inconsistencies and reduce the effectiveness of the learned model [@Pandi23].
+
+Despite the potential of the DFL paradigm, research on securing such environments is still limited, with most existing works focusing on centralized FL approaches [@Mothukuri21]. Many of these approaches utilize Byzantine-robust aggregation algorithms designed to mitigate/filter malicious attacks from adversarial nodes in the central server [@Blanchard17; @Pillutla22]. This raises the question: are these algorithms proposed for centralized scenarios equally efficient in decentralized environments?
+
+In light of these challenges, we introduce an innovative Byzantine-robust aggregation algorithm designed to ensure the proper performance of Decentralized Federated Learning environments, coined [WFAgg]{.sans-serif}, something that, to the best of our knowledge, has not been proposed before. This algorithm is designed to filter and mitigate Byzantine attacks by using different techniques that adapt better to the dynamic conditions of decentralized scenarios. In summary, the contributions of this paper are as follows:
+
+- We design and develop a novel Byzantine-robust aggregation algorithm for DFL environments. It ensures robustness against malicious attacks by using different lightweight filtering techniques, which is particularly suited for adversarial decentralized IoT environments.
+
+- We systematically evaluate different state-of-the-art (SOTA) centralized Byzantine-robust aggregation algorithms in decentralized settings and compare these results to those obtained in the centralized ones.
+
+- We assess the robustness of the proposed algorithm through multiple experiments on an image classification problem under different Byzantine attacks. This algorithm obtains better accuracy and model consistency results compared with the centralized ones under the evaluated tests.
+
+- We implemented a DFL simulator in Python language to conduct the aforementioned experiments.
+
+The remainder of this paper is structured as follows. Section [2](#sec:review){reference-type="ref" reference="sec:review"} includes a detailed explanation of concepts such as Federated Learning and Decentralized Federated Learning, focusing on the current state of the art of the latter. Section [3](#sec:security){reference-type="ref" reference="sec:security"} provides a study of security in FL environments, along with the most well-known techniques for addressing the associated challenges. Section [4](#sec:methodology){reference-type="ref" reference="sec:methodology"} presents the proposed Byzantine-robust aggregation algorithm solution in this paper, providing context for the scenario addressed and details about employed techniques and mechanisms. Section [5](#sec:implementation){reference-type="ref" reference="sec:implementation"} details the simulation settings, including configuration parameters and specifications. Then, Section [6](#sec:results){reference-type="ref" reference="sec:results"} discusses the obtained results regarding the system's robustness and convergence (both in the proposed algorithm and in the SOTA algorithms for centralized schemes). Finally, Section [7](#sec:conclusions){reference-type="ref" reference="sec:conclusions"} offers the final conclusions of this paper, highlighting the contributions to the field of DFL research and suggesting possible future lines of work.
+
+Originally introduced by Google in 2016 [@McMahan17], the classical version of federated learning involves each client maintaining a local model trained with its own training dataset, while a central aggregator (or parameter server) maintains a global model that is updated through the local models of the clients. In depth, centralized FL proceeds as follows [@Li21] (in a specific round): (i) the server sends the current global model parameters to all client devices, (ii) each client updates its local model by performing one or several steps of an optimization algorithm (in the literature, the most common is Stochastic Gradient Descent) using the current global model parameters and their local training datasets, then, (iii) devices send their updated local models back to the server where (iv) the server aggregates these local models from the clients using a specific aggregation rule to compute a new global model for the next round. The most recognized aggregation rule in FL field is Federated Average [FedAvg]{.sans-serif}. This process is carried out in multiple communication rounds, an iterative process that continues until the global model reaches the desired level of accuracy.
+
+Of particular interest in this work is the novel paradigm of Decentralized Federated Learning which, despite not being the predominant approach in this field, introduces certain features that make it a promising alternative. In DFL, various nodes/clients collaborate and train their local models collectively without the necessity of a central server [@Kairouz21; @He18], distributing the aggregation process among the nodes of the learning network. This eliminates the need to share a global model with all clients [@Geiping20].
+
+One of the key advantages of DFL is promoting scalable and resilience environments, allowing nodes to join or disconnect from the learning network based on the power resources of the devices. Unlike centralized approaches, which can become bottlenecks or single points of failure, DFL distributes the workload over the entire network, improving fault tolerance and making the system more robust against attacks [@Beltran23]. Another significant advantage of DFL over centralized approaches is the reduction in communication resources and the decrease in high bandwidth usage due to the elimination of intermediate model/gradient transmissions between clients and the server [@Yuan24]. This is particularly relevant in environments with a large number of interconnected devices.
+
+Despite the significant potential of DFL, research on this topic remains sparse, indicating it is an emerging field. This scenario must confront new challenges absent in centralized approaches due to the decentralized learning process and decision-making across numerous devices. For example, designing communication protocols that define how clients exchange their models in order to prevent excessive communication overheads. Gossip Learning is a notable decentralized communication protocol for asynchronous updates between nodes, leveraging peer-to-peer (P2P) schemes to achieve scalability and potential privacy preservation [@Ormandi13; @Prabhakar22]. Although effective, gossip learning has a slower aggregation evolution due to its fully distributed nature [@Yuan24]. Hybrid protocols combining gossip and broadcast techniques offer greater adaptability to various decentralized scenarios, facilitating model sharing only with neighboring nodes [@Abdelghany22]. Other works [@Beltran23; @Nguyen21] propose blockchain-based FL environments that provide trustworthy schemes by guaranteeing the immutability of exchanged models through a peer-to-peer consensus mechanism.
+
+Moreover, DFL approaches lead to high inconsistency among local models since there is no global model. Some proposals, such as increasing gossip steps in local communications, achieve better consensus [@Shi23]. Another approach involves a consensus-based DFL algorithm inspired by discrete-time weighted average consensus frameworks [@Giuseppi22]. Additionally, techniques like optimizing the consensus matrix to enhance the convergence rate and architectures focusing on continuous consensus over aggregated models are explored [@Du23; @Schmid20].
+
+# Method
+
+Due to the importance they will have throughout this document, we are going to describe some of the most renowned Byzantine-robust aggregation schemes for CFL in the state of the art. Suppose a server receives a subset of models $\{\theta_k\}_{k=1}^{K}$ from its $K$ clients.
+
+- [Mean]{.sans-serif} [@McMahan17]: This is the most basic aggregation rule and often serves as a benchmark for comparisons. This algorithm aggregates client models by calculating the coordinate-wise arithmetic mean of the given set of models. It simplifies the [FedAvg]{.sans-serif} algorithm which uses a weighted average based on the number of samples trained by each client.
+
+- [Median]{.sans-serif} [@Yin18]: This algorithm is based on a coordinate-wise aggregation rule to compute the global model. To achieve this, the server calculates each $i$-th parameter of the model by sorting the $K$ values from the received models and computing their median value.
+
+- [Trimmed-Mean]{.sans-serif} [@Yin18]: This is a variant of the [Mean]{.sans-serif} aggregation algorithm and another coordinate-wise aggregation rule. For a given trim rate $\beta \in \left ( 0, 1/2 \right )$, the server sorts the $K$ received values of each $i$-th parameter in the model and removes the smallest and largest $\beta K$ values. So, the $i$-th parameter of the global model is computed as the mean of the remaining $(1-2\beta)K$ values.
+
+- [Krum]{.sans-serif} and [Multi-Krum]{.sans-serif} [@Blanchard17]: This algorithm filters the models received from clients using Euclidean distances. For each client, the server assigns a score by calculating the sum of Euclidean distances from the client's model $\theta_k$ to the $K-M-2$ closest neighbours models, where $M$ is the number of malicious models. The model with the lowest score is selected as the global model. The [Multi-Krum]{.sans-serif} variant selects the $m$ models with the lowest scores and computes the global model as the mean of these $m$ selected models.
+
+- [Clustering]{.sans-serif} [@Li21; @Sattler20]: The aim of this algorithm is to separate the models received from clients into two clusters and aggregate as the global model those belonging to the larger cluster. This is achieved using an agglomerative clustering algorithm with average linkage, employing pairwise cosine similarities between models as a distance metric.
+
+The majority of existing Byzantine-robust aggregation schemes (including those described previously) can be categorized based on the techniques employed by them [@Shi22]:
+
+- **Statistics:** These schemes utilize the statistical characteristics of updates, such as the median, to block abnormal updates and achieve robust aggregation. They are suitable only when the number of malicious users is less than half of the total users.
+
+- **Distance:** These schemes aim to detect and discard bad or malicious updates by comparing the distances between updates. An update that is significantly different from others is considered malicious. These are suitable only to resist attacks that produce noticeably abnormal updates.
+
+- **Performance:** In these schemes, each update is evaluated over a clean dataset provided by the server, and any update that performs poorly is given low weight or removed directly. These schemes are more reliable than other solutions but rely on a clean dataset for evaluations, which is not always possible.
+
+
+
+Decentralized Federated Learning framework architecture
+
+
+Following the previous subsection, not all proposals in the state of the art fit into the aforementioned classification. This is due to the development of innovative techniques that offer different approaches compared to the more well-known aggregation algorithms.
+
+[SignGuard]{.smallcaps} [@Xu23] is a novel approach which enhances FL system robustness against model poisoning attacks by utilizing the element-wise sign of gradient vectors. It proposes processing the received gradients to generate relevant magnitude, sign, and similarity statistics, which are then used collaboratively by multiple filters to eliminate malicious gradients before the final aggregation. The key idea is that the sign distribution of sign-gradient can provide valuable information in detecting advanced model poisoning attacks.
+
+[FLTrust]{.smallcaps} [@Cao22] proposes an alternative method by implementing a trust-based approach. Unlike the previous proposal, this method uses an initial trust model, trained on a small maintained dataset, to evaluate and weight the contributions of clients in the global model aggregation process. Thus, client updates that align closely with the server model's direction are deemed trustworthy, while others are adjusted or discarded based on their deviation.
+
+There are also other proposals that do not only focus on the geometric properties of models for mitigating Byzantine attacks. For example, [@Li21] proposes a method to mitigate the impact of Byzantine clients in federated learning through a spatial-temporal analysis approach. Apart from leveraging clustering-based methods to detect and exclude incorrect updates based on their geometric properties in the parameter space, the temporal analysis provides a more effective defense mechanism, which is crucial in environments where attack strategies may evolve during the course of model training. In this case, time-varying behaviors are addressed through an adjustment of the learning rate parameter.
+
+Another example is [Siren]{.smallcaps} [@Guo21] which proposes a FL system to improve robustness through proactive alarming. Clients alert abnormal behaviors in real-time using a continuous monitoring scheme based on the evolution of the global model's accuracy over the rounds. When a potential threat is identified, the clients issue an alarm, and the central server adjusts the model aggregation to mitigate the impact of malicious contributions.
+
+Despite the existence of multiple Byzantine-robust proposals in the state of the art, the majority have been oriented towards centralized FL scenarios. To our knowledge, there is no proposed Byzantine-robust aggregation algorithm specifically designed to enhance security in DFL environments. In this section, we elaborate on the system configuration employed in DFL and detail all technical specifications of the proposed algorithm [WFAgg]{.sans-serif} designed to ensure Byzantine-robustness within this framework. Additionally, the corresponding filtering and aggregation algorithms that are integral components of the [WFAgg]{.sans-serif} algorithm will be described in the subsequent subsections.
+
+We consider a cross-device scenario where multiple IoT devices (or servers) carry out a distributed collaborative learning of a machine learning model through DFL. Figure [1](#fig:scenario){reference-type="ref" reference="fig:scenario"} illustrates the proposed scenario for the DFL system. This figure showcases a set of clients/devices $\mathcal{C} =\{C_i\}, \ i = 1, 2, \dots, N$ involved in the collaborative training of a ML model. Each client possesses a local dataset $\mathcal{D}_i, i \in [N]$, with $n_i$ data samples, which is used for training their own local model, $\theta_i$. The objective of FL can be formulated as a non-convex minimization problem: $$\begin{align}
+ \min_{\theta \in \mathbb{R}^d} \mathcal{L}(\theta, \mathcal{D}) = \min_{\theta \in \mathbb{R}^d} \biggl \{ \sum_{i \in [N]} \dfrac{|\mathcal{D}_i|}{|\mathcal{D}|} \mathcal{L}(\theta, \mathcal{D}_i) \biggr \},
+\end{align}$$ where $\mathcal{L}(\theta, \mathcal{D})$ is the empirical loss function of the model $\theta$ over the overall dataset $\mathcal{D} = \bigcup_{i \in [N]} \mathcal{D}_i$ and $|\mathcal{D}| = \sum_{i \in [N]} |\mathcal{D}_i|$ is the total number of samples across all clients.
+
+The topology of the DFL scenario is modeled as an undirected graph $G=(\mathcal{C},\mathcal{E})$ where $\mathcal{C}$ represents the devices participating in the learning process as vertices, which are interconnected by the set of links $\mathcal{E} \subseteq \mathcal{C} \times \mathcal{C}$. Although the topology is dynamic across rounds, the problem is simplified by assuming a static topology where each node consistently communicates with the same set of nodes.
+
+The majority of the client set $\mathcal{C}$ consists of benign clients, i.e., they follow the established protocol and send honest models to the corresponding nodes, while a small fraction are Byzantine clients, i.e., they act maliciously and send arbitrary models with the intent of disrupting the proper training of the model. Nevertheless, this fraction of malicious nodes is relative since each node communicates with a different subset of nodes.
+
+After the training process on its own dataset, each node shares its local model with other nodes in the network. The communication protocol proposed for this framework is based on a Gossip-inspired protocol. Instead of sharing its model with only one random node in the topology, each client forwards the trained model to all of its neighbors in the communication network. The set of neighboring clients of client $C_i$ in topology graph $G$ is defined as $\mathcal{N}_i(G) = \left \{ C_j \in \mathcal{C}, C_j \neq C_i \ : \ \{ C_i, C_j\} \in \mathcal{E} \right \}$.
+
+Upon receiving the trained models from neighboring nodes (assuming receipt of all models), each node applies a Byzantine-robust aggregation algorithm in order to update its local model. This process is repeated iteratively, beginning again with the local model training in the next round.
+
+
+
+Workflow of Byzantine-robust aggregation algorithm WFAgg
+
+
+This proposal suggests, similarly to other approaches, performing a preliminary filtering of the received models before aggregating them. The [WFAgg]{.sans-serif} algorithm defines both procedures, and this can be seen in Figure [2](#fig:byzantine-robust-algorithm){reference-type="ref" reference="fig:byzantine-robust-algorithm"} that summarizes the workflow of the Byzantine-robust aggregation algorithm.
+
+In the [WFAgg]{.sans-serif} algorithm, the set of received models $\mathcal{T}$ by a client (from its neighboring nodes) is passed through multiple filters, each of them with a different purpose. The aim is to perform a distinct type of criteria in each filter using various well-known techniques in this field for mitigating or eliminating malicious models. This approach seeks to compensate for the inadequacies of some techniques in detecting specific attacks by leveraging other techniques. This fact is especially relevant in adversarial decentralized environments, as the number of received models can be low and the fraction of malicious nodes can be high (depending on the network topology).
+
+Specifically, the algorithm [WFAgg]{.sans-serif} comprises three multipurpose filters, including a distance-based filter, a similarity-based filter, and a temporal-based filter. As reviewed in Section [2](#sec:review){reference-type="ref" reference="sec:review"}, several Byzantine-robust aggregation algorithms align with the described characteristics and could be utilized (e.g., [Multi-Krum]{.sans-serif} or [Clustering]{.sans-serif}). However, others such as those based on centrality statistics do not perform a filtering of the models *per se* and therefore would not be suitable (e.g., [Median]{.sans-serif}). Despite this, new algorithms for each of these filters, [WFAgg-D]{.sans-serif}, [WFAgg-C]{.sans-serif} and [WFAgg-T]{.sans-serif}, will be presented and detailed in the following subsections.
+
+::: algorithm
+:::
+
+Once each of these filters has selected a subset of the received models identified as benign ($\mathcal{T}_1, \mathcal{T}_2, \mathcal{T}_3$, respectively for each filter), the model aggregation process will be performed through a weighting of the different filter results. Specifically, each model is scored based on which filters, if any, have identified it as benign. The weighting assigned by each filter is predefined based on the relative importance attributed to that technique in filtering malicious models ($\tau_1, \tau_2, \tau_3$, respectively for each filter, so $\sum_i \tau_i = 1$).
+
+Considering that the algorithm may be attacked by targeting specific characteristics of some filters, it is understandable that a model must be accepted by two or more filters to be considered benign. This design decision is also influenced by the temporal-based filter, as it, by considering only temporal statistics, might identify a constant attack by a malicious node as benign.
+
+Several examples are proposed for better understanding (suppose client $C_i$ is performing the algorithm). If a model $\theta_j$, from one of its neighboring nodes, has been identified as benign by all three filters, it receives the highest weighting in the aggregation process and will be more relevant ($w_{ij} = \tau_1+\tau_2+\tau_3$). Now suppose that the same model is identified as benign by the distance-based and similarity-based filters, so it will receive a lower weighting than in the previous case, resulting in less relevance in the aggregated model ($w_{ij} = \tau_1+\tau_2$). Conversely, if the model is identified as benign only by the similarity-based filter will not be considered in the aggregation process, i.e., it is assigned a zero weight ($w_{ij} = 0$).
+
+The output of the filtering process consists of the triplet $(\mathcal{T}_1, \mathcal{T}_2, \mathcal{T}_3)$, representing the set of models considered benign in this round by each of the filters. Using the weights assigned to each model and the client's own model, a coordinate-wise weighted aggregation (such as [FedAvg]{.sans-serif}) is performed, with the difference that this algorithm assigns a differential value to the local model. This proposed aggregation algorithm is called [WFAgg-E]{.sans-serif} and will be presented and described in the following subsections.
+
+In summary, Algorithm [\[alg:WFAgg\]](#alg:WFAgg){reference-type="ref" reference="alg:WFAgg"} describes the main characteristics of the algorithm functionality.
+
+The [WFAgg-D]{.sans-serif} algorithm (which is described in detail in Algorithm [\[alg:WFAgg-D\]](#alg:WFAgg-D){reference-type="ref" reference="alg:WFAgg-D"}) is proposed as a Byzantine-robust aggregation algorithm aimed at detecting and mitigating models that geometrically differ from the rest. Specifically, it seeks to discard models that are at a greater Euclidean distance from a reference model. This reference model is characterized by the central statistical characteristics of the received models, specifically the information provided by the median model of the set of models.
+
+First, the median (using the [Median]{.sans-serif} algorithm explained in Section [2](#sec:review){reference-type="ref" reference="sec:review"}) of the set of models received from neighboring nodes is calculated. Then, all Euclidean distances between each of the neighbor models and the reference model are computed. From these metrics, the $K-f-1$ models with the smallest Euclidean distance to the reference model, $\mathcal{T}_1$, are selected, where $K$ is the number of received models and $f$ is the number of malicious nodes.
+
+The computational complexity of the algorithm is $\mathcal{O}(d K \log K)$ for the median calculation since it involves sorting the $K$ values of the received models, $\mathcal{O}(K \log K)$, for each of the $d$ components of the model. The complexities of calculating Euclidean distances and selecting the best results correspond to $\mathcal{O}(d K)$ and $\mathcal{O}(K)$ (on average, using a selection algorithm like `Quickselect`), respectively, which are terms dominated by the median calculation (for large values of $K, d$).
+
+::: algorithm
+:::
+
+The [WFAgg-C]{.sans-serif} algorithm (which is described in detail in Algorithm [\[alg:WFAgg-C\]](#alg:WFAgg-C){reference-type="ref" reference="alg:WFAgg-C"}) is proposed as another Byzantine-robust aggregation algorithm with the same objective as the [WFAgg-D]{.sans-serif} algorithm but a different criteria. Unlike [WFAgg-D]{.sans-serif}, which explores the Euclidean distances between models, [WFAgg-C]{.sans-serif} seeks to discard models that represent a significant change in direction relative to a reference model. Similarly, this reference model is characterized by the median of the set of received models. To quantify these directional changes the cosine distance is employed, a metric that measures the similarity between two vectors in a multidimensional space, defined as $$\begin{align}
+\alpha_{\mathbf{a},\mathbf{b}} := 1 - \cos(\angle\{\mathbf{a},\mathbf{b}\}) = 1 - \dfrac{\langle \mathbf{a},\mathbf{b} \rangle}{\lVert\mathbf{a}\rVert \cdot \lVert\mathbf{b}\rVert},
+\end{align}$$ for two vectors $\mathbf{a}, \mathbf{b} \in \mathbb{R}^d$. Remark that $\alpha_{\mathbf{a},\mathbf{b}} \in [0,2]$.
+
+First, the median (using the [Median]{.sans-serif} algorithm explained in Section [2](#sec:review){reference-type="ref" reference="sec:review"}) of the set of models received from neighboring nodes is calculated. Additionally, the median magnitude is calculated to perform magnitude clipping on the models (the magnitude is not a relevant parameter in this algorithm). Then, all cosine distances between each of the neighbor models and the reference model are computed. From these metrics, the $K-f-1$ models with the smallest cosine distance to the reference model, $\mathcal{T}_2$, are selected, where $K$ is the number of received models and $f$ is the number of malicious nodes. The computational complexity of the algorithm can be denoted as $\mathcal{O}(d K \log K)$. This matches the complexity of the previous algorithm since the calculation of the median model remains the dominant term (the computation of the inner product is linear with $d$).
+
+::: algorithm
+:::
+
+The [WFAgg-T]{.sans-serif} algorithm (which is described in detail in Algorithm [\[alg:WFAgg-T\]](#alg:WFAgg-T){reference-type="ref" reference="alg:WFAgg-T"}) is proposed as a model filtering algorithm aimed at detecting nodes that make abrupt behavioral changes in models between rounds. It seeks to identify if a node has conducted a Byzantine attack in a particular round or if the nature of such an attack is semi-random, causing sudden model changes between rounds. Therefore, this algorithm cannot be considered for a Byzantine-robust aggregation scheme because if a malicious node perpetrates a well-designed attack with little variability between rounds, will not be detected.
+
+The comparison of a node's current model with its models sent in previous rounds (which should be non consecutive) is done using geometric metrics, specifically, Euclidean distance and cosine similarity. When analyzing the temporal changes between rounds for a node, a time window of the last rounds is considered to extract the necessary metrics. Each node only needs to store the history of the distance metrics and only the last model sent by each neighboring node. Both the mean and the variability within this time window are studied to compare with the latest model update.
+
+::: algorithm
+:::
+
+The algorithm requires an initial transient period of $T_\mathsf{th}$ rounds during which it does not perform any classification. This is due to two reasons: the first and most obvious is the lack of sufficient data to determine the time window, and the second is that, due to the random and uncoordinated initialization of model parameters at each node, variations are significantly abrupt and could be mistakenly identified as attacks.
+
+A time window of the last $W$ models of the node is taken as a reference, specifically, the last $W$ metrics of Euclidean distance and cosine similarity between the node's model updates. From these metrics, both the mean and the standard deviation are extracted by applying an Exponentially Weighted Moving Average (EWMA), i.e., the most recent metrics in the time window are considered, which is useful since patterns change over time (non-stationary). Subsequently, new metrics are calculated with the model received in the current round, and it is observed if they fall within the thresholds calculated with the mean and standard deviation. If any of them do not meet the criteria, the model update is considered abrupt and malicious in that round.
+
+The [WFAgg-E]{.sans-serif} algorithm is proposed as a model aggregation algorithm similar to the [FedAvg]{.sans-serif}. Unlike [Mean]{.sans-serif} and other variants, it performs a weighted average, giving more or less relevance to the local model of the node or to the updates from neighboring nodes. This aggregation algorithm is designed for adversarial environments so that if a malicious model is selected for aggregation, its influence is mitigated.
+
+The fundamental idea of [WFAgg-E]{.sans-serif} consists of a variation of the first-order exponential smoother [@Montgomery11] (in other fields it is called Exponential Moving Average). It started to become popular in the 1960s and is a technical indicator used in financial analysis to analyze and smooth time series data. It assigns more weight to the most recent data, making it more sensitive to recent changes.
+
+Using the [WFAgg-E]{.sans-serif} algorithm, each client $C_i$ updates its model using its own model and the models received from its neighboring nodes, which have been previously weighted, as follows: $$\begin{align}
+ \notag
+ {\overline{\theta}}_i &= (1-\alpha) \theta_i + \alpha \cdot \dfrac{\sum_{j \in \mathcal{N}_i(G)}{ w_{ij} \theta_j }}{\sum_{j \in \mathcal{N}_i(G)}{ w_{ij} }}\\
+ &= (1-\alpha) \theta_i + \alpha \cdot \sum_{j \in \mathcal{N}_i(G)}{ w'_{ij} \theta_j},
+\end{align}$$ where $\mathcal{N}_i(G)$ is the set of neighboring nodes of $C_i$ given the topology modeled by a graph $G$, $w_{ij}$ is the weight assigned by $C_i$ to the model of client $C_j$, $w'_{ij} \in [0,1]$ is the normalized weight (such that $\sum_{j \in \mathcal{N}_i(G)}{w'_{ij}}=1$), and $\alpha \in [0,1]$ is a smoothing factor that regulates the level of aggregation of the neighbors' models versus the current local model.
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diff --git a/2410.00149/paper_text/intro_method.md b/2410.00149/paper_text/intro_method.md
new file mode 100644
index 0000000000000000000000000000000000000000..e6d129d32800e53bf3e83c63d53c328f1c40e1b5
--- /dev/null
+++ b/2410.00149/paper_text/intro_method.md
@@ -0,0 +1,120 @@
+# Introduction
+
+With the constant influx of information, we need efficient models capable of summarizing essential content from lengthy documents for faster comprehension and prioritization [@personalized-summarization-utility]. Yet, defining what constitutes "salient" information remains subjective, particularly in documents covering multiple aspects. To tackle this, contemporary summarizers should be personalized to users' preference histories and interests.
+
+**Specialized PLMs as summarizers.** @pens proposed the most direct method to train models to learn personalization using user reading histories. These models (called PENS models) use variants of pointer-generator networks [@pointer-gen] that are injected with representations of user reading history for user preference alignment. Other indirect approaches include aspect-based models [@aspect-based-4; @aspect-based-2] that produce summaries coherent with the document themes but lack adaptability to changes in the reader's profile. In contrast, interactive human-feedback-based models allow for iterative refinement based on user feedback, thereby better personalization [@adaptivesum].
+
+**LLMs as personalized summarizers.** Recent studies on the state-of-the-art (SOTA) LLMs show unprecedented In-Context Learning (ICL) based summarization performance [@ICL-summarization-news; @ICL-summarization-meeting; @ICL-summarization-dialogue]. This opens the possibility of In-Context Personalization Learning (ICPL) in these LLMs. At the same time, it also underscores the necessity for robust and dependable methods of evaluating the degree of ICPL within such models. Although benchmarking of LLMs for summarization has been done for accuracy, fluency, and consistency [@LLM_Sum_Eval], so far, no study has been done yet on the probing and evaluation of ICPL in LLMs for the summarization task. In this paper, we propose `iCOPERNICUS`, an **I**n-**Co**ntext **Per**sonalization Lear**ni**ng S**c**r**u**tiny of **S**ummarization capability in LLMs.
+
+**`iCOPERNICUS` framework.** `iCOPERNICUS` is a prompt-based probing framework that investigates whether LLMs exhibit true ICPL using a 3-pronged approach: (i) whether few-shot prompting of **examples (i.e., reader-generated gold references) enhances ICPL**, (ii) whether adding reader's **reading history improves ICPL**, and (iii) whether **contrastive profile information** showing subjective differences in reader-preferences for the same document **induce better ICPL**. Since `iCOPERNICUS` is a *comparative* framework, it needs a personalization measure for analyzing the influence of the injected profile information in the prompts. We use EGISES-JSD, the only known measure for personalized summarization [@egises]. EGISES measures the ability of models to discern the differences in user profiles and generate summaries that are proportionately different.
+
+::: {#tab:LLMs Probed}
++----------------------------------------------------------------------------------------------------+
+| **Model Variants Probed** |
++:=====================+:====================================================+:=====:+:=====:+:=====:+
+| **Base-** | Llama 2 (7B, 13B) [@llama-2-base-underfitting] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| **models** | Mistral v0.1 (7B) [@{mistral-(GQA-SWA)}] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| **Instruct-** | Mistral 7B Instruct v0.1 [@{mistral-(GQA-SWA)}] | | | |
+| +-----------------------------------------------------+-------+-------+-------+
+| | Mistral 7B Instruct v0.2 [@{mistral-(GQA-SWA)}] | | | |
+| +-----------------------------------------------------+-------+-------+-------+
+| | Tulu V2 (7B, 13B) [@Tulu] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| | Orca 2 (7B, 13B) [@orca-2] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| | Stable Beluga (7B, 13B) [@stable-beluga-2] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| **RLHF-tuned** | Llama 2 Chat (7B, 13B) [@llama-2-base-underfitting] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+| **DPO-tuned** | Tulu V2 DPO (7B, 13B) [@Tulu] | | | |
+| +-----------------------------------------------------+-------+-------+-------+
+| | Zephyr 7B $\alpha$ [@Zephyr] | | | |
+| +-----------------------------------------------------+-------+-------+-------+
+| | Zephyr 7B $\beta$ [@Zephyr] | | | |
++----------------------+-----------------------------------------------------+-------+-------+-------+
+
+: LLMs probed for ICPL w.r.t summarization.
+:::
+
+**Observations** As a case-study of the application of `iCOPERNICUS`, we probe ten SOTA LLMs that exhibit reasonably good ICL on standard benchmark tasks, six of which have 7B and 13B model size variants (see Table [1](#tab:LLMs Probed){reference-type="ref" reference="tab:LLMs Probed"}). We use the PENS dataset [@pens] as in [@egises] to compare the ICPL results with the baseline specialized personalized summarization models evaluated therein. We observe that all the studied models, except for Orca-2 7B and Zephyr 7B $\beta$, exhibit performance degradation in all the three probes of `iCOPERNICUS` - i.e., injection of examples, history, and contrastive profile information.
+
+**Key Contributions.** We present for the first time: (i) a detailed introduction of ICPL for personalized summarization, (ii) `iCOPERNICUS` as a formal framework of evaluation of ICPL in LLMs, and (iii) a detailed case-study of the application of `iCOPERNICUS` tests for determining ICPL in the selected SOTA LLMs.
+
+# Method
+
+As proposed in @egises, the *degree-of-personalization* is a quantitative measure of how much a summarization model fine-tuned for personalization is adaptive to a user's (i.e., reader's) subjective expectation. This also implies that it measures how accurately a model can capture the *user's \"evolving\" **profile** reflected through **reading history*** (i.e., a temporal span of the reading and skipping actions of a user on a sequence of documents that is interleaved by the actions of generating and reading summaries). This is because the *subjective expectation is a function of the reading history*. **A low degree of personalization, by definition, implies poor user experience (UX)**. If a model does not efficiently capture the user's profile, it may lead to irrelevant summaries. In this situation, poor UX would mean that the user would have to spend more time getting to the information he/she is interested in or suffer from information overload and fatigue. However, this irrelevant information can be useful for a different user with a different profile. To illustrate this, we borrow the example given by @egises where if reader Alice, who has been following \"*civilian distress*\" in the Hamas-Israeli conflict, reads a news summary whose content is primarily about \"*war-front events*\", her UX will drop down due to information overload and high time-to-consume, even though her interest is also covered to a fair extent. In contrary, a reader Bob, who has been mostly following war news, would have quite high UX.
+
+@egises showed theoretically and empirically that a model could have high accuracy scores in both the examples in the previous section, although the individual degree of personalization differs. This can *mislead selection of a model for a fairly high accuracy score even though it suffers from poor UX*. To address this, they proposed EGISES as, to the best of our knowledge, the only known measure for personalized summarization evaluation. Informally, EGISES measures the extent to which a model can discern the differences between user profiles and generate summaries that have proportionate differences. Difference is modeled as a chosen divergence on a metric space. See Appendix [9.2](#app: egises){reference-type="ref" reference="app: egises"} for formulation.
+
+In-Context learning (ICL) is an emergent phenomenon exhibited LLMs (first highlighted in @ICL-few-shot-GPT3 for GPT-3), where models acquire proficiency in unknown tasks on which they are not pre-trained from limited examples, called *prompts*, with no update in their parameters (i.e., the models are frozen).[^3] In-Context Personalization Learning (ICPL) for summarization is a special case of ICL where, for a document query $d_q$, an LLM is expected to generate the user-specific optimal summary $s^*_{(d_q, h_j)}$, for the $j$-th user expecting the summary of $d_q$. Here, $h_j$ is the user's **reading history** (temporal sequence of the user's click and skip history of documents). $s^*_{(d_q, h_j)}$ is the same as the $j$-the user's expected summary $u_{qj}$ (i.e., gold-reference), and hence can be denoted $s^{*}_{u_{qj}}$.
+
+::: {#def:prompt4ICPL .definition}
+**Definition 1**. ***Prompt4ICPL**: A prompt $\mathcal{P}_{ICPL}$ to a language model $M$ consists of an user's reading history ($h$), an optional sequence of $n$ concatenated ($\oplus$) demonstration examples (i.e., input-label pairs) as: $h_{j}\oplus \bigoplus\limits_{i=1}^{n} (d_{i} \oplus u_{ij})$ where, $d_i$ is the example document to be summarized for $j$-th user ($u_{ij}$ being the **gold-reference summary example**), and a query document $d_q$, such that $d_{i} \neq d_q$.*
+:::
+
+A **zero-shot prompt (0-shot)** is the special case when demonstration examples are not provided (i.e., $u_{ij}=\emptyset$) while $d_q$ and the user reading history $h_j$ are given in the prompt. The few-shot version can be of two types: (i) **with history (k-shot w/hist.)**, and (ii) **without history (k-shot w/o hist.)**. In the second type, the user profile is not represented by reading history but rather by the expected summaries (or gold-reference summaries). This can seen as the user's "*writing history*\".
+
+`iCOPERNICUS` is a novel prompt-based three-pronged probing framework for evaluating ICPL in LLMs. It tests *whether the model can harness **three types of profile information** included within the test prompts: (i) **examples**, (ii) **history**, and (iii) **contrastive information** (in terms of history and examples)*. Before we provide a detailed outline of the framework, we first discuss the contrastive probing setup in the following section.
+
+One of the key probes of the `iCOPERNICUS` framework is testing LLMs for ICPL with *contrastive examples*, i.e., input-label pairs containing at least two user (i.e., reader) profiles (can be reading or writing history) with the query document $d_q$. An LLM capable of ICPL should be able to discern the difference between the reader profiles and generate summaries accordingly (i.e., $s_{u_{ij}}$ and $s_{u_{ik}}$) in line with the notion of insensitivity-to-subjectivity as defined by @egises (see Appendix [9.1](#app:insensitivity){reference-type="ref" reference="app:insensitivity"}). Contrastive Prompt4ICPL ($\mathcal{P_C}$) is defined as:
+
+
+
+𝒫𝒞 type: Contrastive (C)-2-shot w/ history.
+
+
+::: {#def:contrastive-prompt .definition}
+**Definition 2**. ***Contrastive Prompt4ICPL ($\mathcal{P_C}$)**: A $\mathcal{P_C}$ given to a language model $M$ is a sequence of $n$ concatenated ($\oplus$) **contrastive** demonstration examples $\mathcal{D}_{\mathcal{P_C}}$ as: $(\bigoplus\limits_{j=1}^{m}h_{j})\oplus (\bigoplus\limits_{i=1}^{n} (d_{i} \oplus \bigoplus\limits_{j=1}^{m} u_{ij}))$), each having $m$ **subjective** expected summaries ($u_{ij}$), and a query document $d_q$, s.t. $d_{i} \neq d_q$.*
+:::
+
+A **contrastive zero-shot prompt (C-0-shot)**, contains $h_{j}$ and $h_k$ representing the contrastive reading-histories of two users $j$ and $k$ with no demonstration examples. The few-shot version, similar to k-shot (plain) prompt, is of two types: (i) **C-k-shot w/hist.** (Figure [1](#fig:C-2-shot-w/ hist.){reference-type="ref" reference="fig:C-2-shot-w/ hist."}) and (ii) **C-k-shot w/o hist**. In the second case, gold-reference summaries (writing history) of **both users** are given in the prompt as examples but not their reading histories.[^4] We now define ICPL (weak and strong cases) as:
+
+::: {#def:weak ICPL .definition}
+**Definition 3**. ***Weak ICPL.** Given a contrastive prompt $\mathcal{P_C}$, an LLM $M_{\boldsymbol{\theta},u}$ exhibits weak ICPL, if $\forall(u_{qj}, u_{qk})$ w.r.t query document $d_q$, $(\sigma(u_{qj}, u_{qk})\leq \tau_{max}^{U}) \iff
+(\sigma(s_{u_{qj}}, s_{u_{qj}}) \leq \tau_{max}^{S})$; $\tau_{max}^{U}, \tau_{max}^{S}$ are bounds within which users' expected (gold-reference) summary pair $(u_{qj}, u_{qk}$) and LLM-generated summary pair ($s_{u_{qj}}, s_{u_{qj}}$) are indistinguishable; $\sigma$ is an arbitrary distance metric on the metric space $\mathcal{M}$, where $d, u, s$ are defined.*
+:::
+
+::: {#def:strong ICPL .definition}
+**Definition 4**. ***Strong ICPL.** Given $\mathcal{P_C}$, $M_{\boldsymbol{\theta},u}$ exhibits strong ICPL, if $\forall(u_{qj}, u_{qk})$ w.r.t $d_q$, $M_{\boldsymbol{\theta},u}$ satisfies: (i) weak ICPL, and (ii) $(\sigma(u_{qj}, u_{qk}) > \tau_{max}^{U}) \iff (\sigma(s_{u_{qj}}, s_{u_{qj}}) > \tau_{max}^{S})$.*
+:::
+
+The following sections describe the three-pronged `iCOPERNICUS` framework.
+
+The first probe within the `iCOPERNICUS` framework studies the impact of k-shot prompts (in contrast to 0-shot prompts) on LLM models.[^5] The (plain) k-shot w/o hist. prompt-based probing investigates whether the model improves ICPL performance w.r.t EGISES-scores by mapping the key latent concepts in each example summary with those in the corresponding document for any given user. This is a much richer cue than the 0-shot case, where the model does not get much assistance but can only observe inter-document conceptual associations (i.e., user's click and skip patterns) provided as a part of the reading history $h$. A richer version of this probe is that with k-shot w/ hist. prompts, where additional history information is also provided to investigate if the model can associate that with the examples provided. These two probes should also be performed in the contrastive prompt setting (C-k-shot prompts (w/ and w/o hist.)) to investigate the presence of the same kind of associations with the additional capacity of associating user-specific profile concepts. More specifically, ideally, a model should be able to **harness example summaries** in the following order of ICPL performance:
+
+1. (plain) 0-shot $\prec$ **(plain) [k-shot]{.underline} w/o hist.**, violation leads to **Paradox 1 (PX-1)**.
+
+2. (plain) 0-shot $\prec$ **(plain) [k-shot]{.underline} w/ hist.**, violation leads to **Paradox 1 w/ hist. (PX-1-h)**.
+
+3. C-0-shot $\prec$ **C-[k-shot]{.underline} w/o hist.**, violation leads to **PX-1 (contrastive)**.
+
+4. C-0-shot $\prec$ **C-[k-shot]{.underline} w/ hist.**, violation leads to **PX-1-h (contrastive)**.
+
+The second probe investigates whether a model can utilize the temporal sequence of a specific user's reading (i.e., document clicking and skipping) history and associate the innate latent concepts with that of the corresponding example summaries provided in the prompt (both plain and contrastive). Hence, a model should be able to **harness reading history** in the following ICPL performance order:
+
+1. (plain) k-shot w/o hist. $\prec$ **(plain) k-shot [w/ hist.]{.underline}**, violation leads to **PX-2**.
+
+2. C-k-shot w/o hist. $\prec$ **C-k-shot [w/ hist.]{.underline}**, violation leads to **PX-2 (contrastive)**.
+
+The third probe investigates whether models can capitalize on additional contrasting (i.e., similarity/differences) information about user profiles in contrastive prompts. Hence, a model should be able to **harness contrastive information** in the following order of ICPL performance:
+
+1. (plain) 0-shot $\prec$ **[C]{.underline}-0-shot**, violation leads to **PX-3**.
+
+2. (plain) k-shot w/o hist. $\prec$ **[C]{.underline}-k-shot w/o hist.**, violation leads to **PX-4**.
+
+3. (plain) k-shot w/ hist. $\prec$ **[C]{.underline}-k-shot w/ hist.**, violation leads to **PX-5**.
+
+::: table*
+ **Prompt Style** **Reading Hist.** **Examples** **Article Body** **\# Prompts**
+ -------------------- ------------------- ---------------- ------------------ ----------------
+ 0-shot 1200 Tokens -- 2500 Tokens 6856
+ C-0-shot 1000 x 2 Tokens -- 1700 Tokens 5246
+ 2-shot w/o hist. -- 950 x 2 Tokens 1800 Tokens 6798
+ C-2-shot w/o hist. -- 950 x 2 Tokens 1800 Tokens 5246
+ 2-shot w/ hist. 1200 Tokens 600 x 2 Tokens 1300 Tokens 6798
+ C-2-shot w/ hist. 850 x 2 Tokens 450 x 2 Tokens 1100 Tokens 5246
+
+[]{#tab:ContextLengthTable label="tab:ContextLengthTable"}
+:::
diff --git a/2410.01627/main_diagram/main_diagram.drawio b/2410.01627/main_diagram/main_diagram.drawio
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index 0000000000000000000000000000000000000000..2a121bfe83df9a4001b38f3e055b87ff0d9b0e69
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diff --git a/2410.01627/main_diagram/main_diagram.pdf b/2410.01627/main_diagram/main_diagram.pdf
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+# Introduction
+
+Task oriented dialogue systems (TODS) have gained significant traction and investment from industry because of their efficiency, accessibility and 24x7 availability to serve customers. Automation through TODS is expected to save billions of dollars in labor costs by 2026 [\(Gartner,](#page-7-0) [2022\)](#page-7-0).
+
+Intent Detection is a vital part of natural language understanding (NLU) layer of TODS. Tra-
+
+
+
+Figure 1: Example of broad/specific intent scopes and OOS queries which Intent Detection systems deal with in a typical TODS.
+
+ditionally, intent detection has been used to understand and map the user query to a bot action (e.g., respond with a static answer, execute a pre-configured flow etc) [\(Dialogflow,](#page-7-1) [2010;](#page-7-1) [LEX,](#page-8-0) [2017\)](#page-8-0). With increasing use of LLMs such as Chat-GPT [\(OpenAI,](#page-8-1) [2022\)](#page-8-1), Claude [\(Anthropic,](#page-7-2) [2023\)](#page-7-2), Mistral [\(Mistral,](#page-8-2) [2023\)](#page-8-2), Llama [\(Meta,](#page-8-3) [2023\)](#page-8-3) as retrieval augmented generators to generate answers to user queries in TODS, intent detection is being used to identify the right knowledge sources, APIs, and tools to call for retrieval augmented generation. This ensures efficient utilization of tools, APIs and various other knowledge sources.
+
+An intent detection system of a conversational AI service is expected to handle intents anywhere in the spectrum of very-broad to very-specific scopes[1](#page-0-0) depending upon actionability of intents and bot usecases as shown in Fig [1.](#page-0-1) They are also
+
+\*Contributed to this work during her internship at Amazon
+
+1By "scope of intent" we mean semantic space of all natural language utterances which can fall in that intent.
+
+
+
+Figure 2: Methodology for adaptive ICL and CoT based intent detection using LLMs.
+
+expected to accurately reject out-of-scope (OOS) queries[2](#page-1-0) without having access to any training data for such queries as universe of OOS queries for any TODS is infinitely large. Since for a typical conversational AI service, data for intent detection training comes from bot developers who are not experts in ML, intent detection systems have to also deal with imbalanced training datasets. Additionally, these systems are expected to work with very few utterances per intent.
+
+Traditionally, intent detection systems have been built using supervised classification or similarity based models [\(Zhang et al.,](#page-8-4) [2021;](#page-8-4) [Liu and Lane,](#page-8-5) [2016;](#page-8-5) [Casanueva et al.,](#page-7-3) [2020\)](#page-7-3). LLMs, due to their few-shot learning capabilities, world knowledge and impressive performance across multiple NLP tasks [\(Qin et al.,](#page-8-6) [2023;](#page-8-6) [Zhao et al.,](#page-8-7) [2023\)](#page-8-7), have the potential to improve intent detection systems in TODS. In this work, we explore how LLMs can be best leveraged for the task of intent detection and assess their ability to handle OOS queries and varying scope of intents.
+
+Contributions. 1. We employ generative LLMs using adaptive in-context learning (ICL) and chain of thought (CoT) prompting for the task of intent detection and compare them against contrastively fine-tuned sentence transformer (SetFit) models, highlighting performance/latency trade-offs. We evaluate 7 SOTA LLMs from Claude and Mistral families on 3 open-source and 3 internal real world datasets.
+
+2. We propose a hybrid system that combines Set-Fit and LLM by conditionally routing queries to LLM based on SetFit's predictive uncertainty determined using Monte Carlo Dropout. We also
+
+propose a negative data augmentation technique that improves SetFit's performance by >5% across datasets. The resulting system achieves performance within ∼2% of native LLM performance with ∼50% less latency than native LLM.
+
+- 3. We study the behavior of adaptive ICL based intent detection through controlled experiments and show that LLM's OOS detection capability significantly depends upon the scope of intent labels (class design) and the number of labels.
+- 4. We also propose a novel two step methodology utilizing internal LLM representations to help improve LLM's OOS detection capabilities and show empirical gains in OOS detection accuracy and F1 score by >5% across datasets for Mistral-7B.
+
+We intend to also share the three internal datasets after necessary approvals as a community resource and to ensure reproducibility.
+
+# Method
+
+We fine tune sentence transformer (SetFit) models in two steps (Tunstall et al., 2022a) and use them as our baseline. In the first step, a sentence transformer model is fine-tuned on the training data in a contrastive, siamese manner on sentence pairs. In the second step, a text classification head is trained using the encoded training data generated by the fine-tuned sentence transformer from the first step.
+
+Negative Data Augmentation. To help SetFit learn better decision boundaries, we augment training data by modifying keywords in sentences by (a) removing, or (b) replacing them with random strings. These modified sentences are considered OOS during training. Since these augmented OOS sentences have similar lexical pattern as in-scope
+
+
+
+| | No. | | No. of Queries | | | | | |
+|---------|---------|-------|----------------|-----|--|--|--|--|
+| Dataset | of | | Valid | | | | | |
+| | Intents | Train | In Scope | OOS | | | | |
+| ALC | 8 | 150 | 338 | 128 | | | | |
+| ADP | 13 | 683 | 803 | 91 | | | | |
+| OADP | 13 | - | 430 | 56 | | | | |
+
+Table 2: Data Statistics for AID3 dataset
+
+training sentences, these are expected to help the model avoid latching onto any spurious patterns and help overall learning.
+
+Fig [2](#page-1-1) shows how we use LLMs with adaptive ICL and CoT prompting for intent detection. During offline processing, we embed all training examples using a sentence transformer model and store the embedding vectors in a DB. Additionally, we generate and store descriptions for every intent from training data using LLM. During inference, we embed the user query using the same transformer model and retrieve top-k most similar queries per intent with similarity >t, where t is retriever threshold. We construct prompt for LLM using retrieved ICL examples, stored intent descriptions and static task specific instructions.
+
+High compute and latency costs of LLMs make them prohibitively expensive to use in production at scale.[3](#page-3-0) Hence, we propose a hybrid system which routes incoming queries to LLMs for intent detection only if SetFit model is uncertain. We sample M predictions from the SetFit model using Monte Carlo (MC) dropout [\(Gal and Ghahramani,](#page-7-5) [2016\)](#page-7-5) and use variance of the predictions as an uncertainty estimate.
+
+We use SOFMattress, Curekart and Powerplay11 datasets from HINT3 [\(Arora et al.,](#page-7-6) [2020\)](#page-7-6). We also use AID3[4](#page-3-1) , a collection of three internal multilabel datasets shown in Table [2](#page-3-2) - ALC, ADP and OADP, each containing diverse set of PII redacted in-scope and OOS real world queries from shopping domain. Both ALC and ADP contain queries
+
+from deployed shopping assistant, whereas OADP contains queries from single turn QnA forum. We use OADP to test out of distribution generalization while using ADP train set. See Appendix [A.1](#page-8-13) for more details on AID3. Label space size across HINT3 and AID3 datasets varies from 8 till 59 and all these datasets are real world intent detection datasets from deployed TODS which mimic real world scenarios and production challenges like handling intents with very-broad to very-specific scopes, imbalanced training datasets with very few examples per intent. By evaluating on HINT3 and AID3 datasets we include scenarios where there are large number of intents (59 being the maximum label space size) and also include multi-label scenarios (3 out of 6 of our datasets are also multi-label), which makes our evaluation more comprehensive.
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+# Method
+
+**Architecture:** We inflate APM's architecture in Table[\[tab:architecture\]](#tab:architecture){reference-type="ref" reference="tab:architecture"}. For the TTT experiments, APM consists of only a single convolutional layer, and 5 MLP layers. Additionally, APM consists of a feature projection head containing a single linear layer, and an optional RGB head. It maybe noted that the number of kernels in the convolutional filter is only 1. This creates a subtle issue: the RGB reconstruction in Fig[\[fig:APM_routing\]](#fig:APM_routing){reference-type="ref" reference="fig:APM_routing"} is black and white. This is because a single kernel loses RGB channel information. Put simply, assume a tuple of 3 numbers representing RGB values, $<1,2,3>$ ,$<4,1,1>$. For a convolution operation with a single kernel assuming unit weights, the answer is $6$. If this $6$ gets injected in the net, it is equally certain that the input was $<1,2,3>$ or a $<4,1,1>$ making reconstruction from the RGB head of APM difficult. We found that this symmetry-breaking issue could be noticeably fixed with $n_{kernels} \geq n_{channels}$, where $n_{channels}$ is the number of channels $c$ in the input. Historically, various other rotational/translational/mirror-symmetries have played an important role in designing boltzmann machines[@sejnowski1986learning]. The architecture in Table [\[tab:architecture\]](#tab:architecture){reference-type="ref" reference="tab:architecture"} is then meant to showcase APM's potential to an extrema: how much can it do *even with a single* convolutional filter?
+
+**Hyperparameters:** All hyper-parameters utilized for APM during test-time-training are detailed in [1](#tab:hparams){reference-type="ref" reference="tab:hparams"}. We leveraged the seed 42 in most of our experiments, and also conducted experiments with multiple seeds. The weight matrices in APM were initilized with from a random distribution with $\mu=0$ and $\sigma= 0.01$. All the code has been written in Pytorch version 1.13.0. We also note that performing test-time-training with $16$ bit floating point allows us to effectively use recent GPU architectures for eg, Ampere: they contain a larger number of tensor cores *in addition* to CUDA cores which results in significant speedups during the exprimentation process. Finally, we normalize an input image using standard Imagenet stats, and *dont resort to any other form of augmentation*, thereby making the pipeline far-simpler.
+
+::: {#tab:hparams}
+ -------------------------- -----------------------------------------------------------------------
+ Number of Test samples 50000 (Imagenet Splits), variable for other datasets.
+ Testing iterations 20
+ Batch Size 1
+ Learning Rate 1e-4
+ Optimizer Adam
+ Feature Output size $d$ $768/1024$
+ Positional Encoding size $768/1024$
+ Image/Crop Size 448
+ Augmentations Normalization, $\mu = (0.485, 0.456, 0.406)$, $\sigma= (0.229, 0.224,
+ 0.225)$
+ Precision fp16 (grad-scaled)
+ Num of Workers 8
+ Operating System 1x rtx a6000 48GB/96GB ram/Ubuntu 22.04/2TB ssd/5TB HDD
+ -------------------------- -----------------------------------------------------------------------
+
+ : **APM hyperparameters** during test-time-training.
+:::
+
+During our experiments in test-time-training, APM relies on higher-dimensional CLS token distilled from a teacher trained on a large-scale-dataset, often via contrastive image-text objectives. We showed quantitative results with CLIP, OpenCLIP and qualitative semantic-clusterings with DinoV2.
+
+CLIP is a zero-shot model from OpenAI which contains a vision encoder, and a textual encoder. The textual encoder tokenises input class names to features. Both image/text encoder project them to common dimensionality, and classification happens by measuring distances in contrastive space, thereby offering a higher degree of freedom, as opposed to training a class-sensitive linear-probe. CLIP VIT-L features an output CLS token of $768$ dimensions, while CLIP VIT-H outputs $1024$ dimensions both of which have been accommodated in Tab[\[tab:architecture\]](#tab:architecture){reference-type="ref" reference="tab:architecture"}. DinoV2[@oquab2023dinov2] is a foundational-model trained via SSL-objectives and predicts significant semantically-aware representations, which are widely used in various downstream computer-vision tasks.
+
+Inductively, both CLIP/Dinov2 rely on VIT, which operates on the principle of parallel attention[@vaswani2017attention]: image-tokens a.k.a patches can flow along parallel paths among different layers stacked over each other without any loss of spatial-resolution which was also a key-shortcoming of convolutional-nets. Even though the transformers have no bottleneck issue, the attention-operation in each layer still occupies a significant amount of memory[@modi2023occlusions].
+
+To evaluate model's robustness to distribution shifts, it is necessary to test them on datasets which contain corruptions on a variety of scenarios for eg, *fog, rain, snow, etc.*. One standard practice has been to take the test set of larger datasets like ImageNet, and create synthetic corruptions to establish appropriate-benchmarks on which the performance of these models can be compared. Alternatively, certain test splits have been manually-curated *from-scratch* over the internet, for eg, sketches, artistic-drawings etc. Below, we detail some of the splits which were used in this paper for APM's robustness experiments.
+
+**Cifar-10C**: is a test-split consisting of $10000$ test-samples of Cifar-10, corrupted with $15$ noises, across $5$ levels of noise-severities. In this paper, we have shown results on the highest level, a.k.a level 5 owing to the resource-constraints.
+
+**Imagenet-C:** ImageNet-C is a dataset split for recognizing objects under distribution shifts, with 1000 classes like original Imagenet. This split contains 15 types of corruptions, with each type containing 5 level of noise severities, aka, the percentage of the image region which is being typically impacted by the corruption.
+
+**ImageNet-V2**: is an independent test set containing images sampled from naturally occuring scenarios, with 10000 images of 1000 ImageNet classes. ImageNet-V2 typically consists of 3 splits, with varying levels of difficulties.
+
+**ImageNet-A**: refers to a curated test-set containing \"natural occurring adversarial samples\", which were misclassified by the Resnet-50. This particular split contains 7500 images of 200 ImageNet categories.
+
+**ImageNet-R**: refers to a novel test-set of several Imagenet categories, which contain artistic renditions. There are 30000 images in this split spanning across 200 ImageNet categories.
+
+**ImageNet-Sketch**: is a challenging test-split which consists of only black-and-white sketches of 1000 ImageNet categories. This split originally consists of 50,000 images in total.
+
+Typically, methods like CLIP evaluate on these ImageNet using a prompt-ensemble of $80$ handcrafted templates. APM results were also shown using this ensemble for fair comparisons.
+
+Here we perform some additional ablations to understand how varying one of the parameters of APM, while holding others hyper-parameters constant impacts the performance during test-time-training.
+
+**Effect of varying number of ttt iterations:** We perform varying number of ttt iterations, evaluate the performance of APM on three seeds, and report the mean and standard deviations. We note that at $t=25$, APM obtains $49.5\%$ accuracy with a minimum observed standard-deviation of $0.2$.
+
+**Effect of varying number of parameters:** In Tab[\[tab:ablation_params\]](#tab:ablation_params){reference-type="ref" reference="tab:ablation_params"}, we perform an additional ablation: the number of parameters inside APM's linear layers are gradually changed from 7M to 120M. We perform TTT iterations on the DTD dataset. As can be observed, the top-1 classification-accuracy of APM increases from $47.0$ to $49.1$, thereby indicating that a $53M$ net was optimal for this particular instance of the problem. Beyond that, we observe a gradual drop in the performance, thereby indicating that the net has started to overfit. In an ideal scenario, we would want that lower number of parameters should yield higher performance. However, this would then require some more fundamental changes which allows the net to achieve higher-bandwidths, which remains a matter for the future work[@hinton2022forward].
+
+**Effect of removing the teacher from APM:** APM can perform test-time-training on a single test-sample by relying on a CLS-token distilled from a teacher trained on-scale. This might lend itself to the assumption that APM *really requires* a teacher in order to learn semantically-aware representations. In this ablation, we remove the teacher entirely and have APM perform RGB-reconstruction on COCO-train set. The $L_2$ RGB-reconstruction loss on COCO-val loss then falls to 0.0027. Training took far longer than if last-layer feature-vectors were also distilled from a teacher into APM.
+
+Higher-dimensional vector-spaces carry more bits[@shannon1948mathematical], but incur significant randomness[@boltzmann2022lectures]. Island/vector-distillation speeds-up the learning-process which otherwise might only be informed via RGB-reconstruction and take a lot of time[@he2022masked; @hinton2023represent]. This helps guide APM's ship to correct points in the subspace. Progressing from VIT b-\>h in Tab[\[tab:imagenet_splits\]](#tab:imagenet_splits){reference-type="ref" reference="tab:imagenet_splits"}, shows APM becomes more competitive thereby validating this intuition.
+
+**Results on Cifar-10C:** In Tab[\[tab:cifar_10_c\]](#tab:cifar_10_c){reference-type="ref" reference="tab:cifar_10_c"}, we show additional results on Cifar 10-C dataset widely used in test-time-training [@ttt_sun]. Cifar-10C consists of $15$ types of noise corruptions for all the 10,000 samples present in the Cifar-10 dataset. As evident from the table, CLIP is *not* naturally robust on Cifar-10 and achieves an error rate as high as $24.5\%$. This is worse than existing ttt-methods like TTT-Online and UDA-SS. Therefore, CLIP VIT-L is *not* naturally-robust on Cifar-10c.
+
+Using it as a teacher, we get the *lowest* average error rate of $14.8\%$, thereby even improving upon the performance of CLIP VIT-L/14. Note that our APM is reinitialized with *random* weights after every-test sample in contrast to methods like TTT-Online which $retain$ the weights after every ttt-iteration. Inspite of that, we get a lower error rate i.e. $14.8\%$ than TTT-Online $19.1\%$. Another benefit which APM gains is that it can directly use the textual-encoder of the Clip VIT-L/14 teacher to classify on cifar-10 test set: this allows us to *bypass* the requirement of training a separate dataset-specific linear probe on top of our net.
+
+**Results on ImageNet-C:** In Tables[\[tab:imagenet_c_level_1\]](#tab:imagenet_c_level_1){reference-type="ref" reference="tab:imagenet_c_level_1"},[\[tab:imagenet_c_level_2\]](#tab:imagenet_c_level_2){reference-type="ref" reference="tab:imagenet_c_level_2"},[\[tab:imagenet_c_level_3\]](#tab:imagenet_c_level_3){reference-type="ref" reference="tab:imagenet_c_level_3"},[\[tab:imagenet_c_level_4\]](#tab:imagenet_c_level_4){reference-type="ref" reference="tab:imagenet_c_level_4"}, we perform test-time-training on APM for different imagenet splits across increasing levels of noise-severities. We observe that APM continues to obtain competitive performance over it's teacher.
+
+
+
+Cifar 10 islands: Individual part-wholes are clearly observed in APM features. These features are used for downstream classification.We leverage the visualization mechanism by . These islands are not been hand-picked
+
+
+
+
+Qualitative Results on COCO Val set: Our APM is trained on COCO-train set, and islands on COCO-val set are visualized. We leverage the visualization mechanism by . Note that these islands are a consequence of single feed-forward through the net and not an iterative routing mechanism as in . These islands are not been hand-picked.
+
+
+APM proposes two technical ideas. 1) The first idea is the proposed column representation $T$ 2) The second idea is the folding-unfolding mechanism. However, there are several deeper non-technical/non-scientific inspirations which motivated the design of APM. We discuss some of those, to help facilitate a deeper-connection and ground our intuitions.
+
+**A biological analogy[@hinton2023represent]**: Consider how an organism starts its existence from a cell. The cell is copied across different body locations. Each location possesses identical DNA. However, depending on the location, the DNA decides whether to form an eye or nose. We term this process as **unfolding**, i.e. a cell 'expands' to yield an organism. Next, there is evidence of jellyfish like *Turritopsis dohrnii* reverting from their fully grown form to younger polyp states [@piraino1996reversing]. We term this process as **folding**, i.e. cells of an organism collapse back to the single cell it began from.
+
+**A computational analogy[@hinton2023represent]:** We now start treating an image $I$ as a digital organism. It starts from some compressed representation $T$. $T$ unfolds to yield the image $I$. $I$ then folds back to yield the compressed representation $T$. Learning proceeds by oscillating between these unfolded and folded phases. At every step, the net is trying to reconstruct image $I$ from $T$. $T$ is then expected to be a dense vector-space.
+
+**A cellular-automaton analogy[@wolfram2002new]:** On surface it seems a pretty trivial matter to discuss: a point can expand and yield beautiful patterns which can either be an entire universe in accordance with the theory of big-bang, or can be reproduction of an organism from a singular zygote. But, it is funny: if you start from a point, and unfold it, then all you can get is a sphere. This appears to be true for the behaviour of light, in accordance to Huygens principle[^5]. However, we observe non-spherical objects around us all the time. Turing posited that the symmetry breaking in the sphere must happen somewhere while the organism unfolds: such patterns could then be explained a variety of diffusion based equations[@ho2020denoising].
+
+This idea has been explored in cellular automaton: different replication rules of starting point can yield different final patterns[^6]. Scientists then continue to derive different rules which yield different patterns, which is akin to how we were resorting to hand-engineering features in deep-learning for a long time. **APM attempts to answer the question: Is it possible to build a learning machine which can start from a point, unfold, and then express correct features at the correct place?**. We want to then push the job of rule-learning to what backpropogation does best. We have lost the \"why\" for the knowledge was encoded in the weights of the net, but we seem to have gained the ability of correct features presenting themselves at correct locations. This location-aware-disentanglement procedure thereby represents a step towards solving Arnold's superposition theoram and Hilbert's thirteenth problem[@hinton2023represent]. However, backpropogation can only approximate solutions and not yet reach exact ones[@hinton2022forward], and mathematical formulations are lost into the weights of the neural-net. We then begin to imagine learning machines[@turing1990chemical; @turing1936computable; @turing2009computing] which can solve a complex problem like cryptography/breaking-a-cipher in two phases 1) relax the system towards an approximate solution [@hinton1984boltzmann] 2) have the system spit-out which parts of the solution are uncertain, and brute-force towards the remaining solution. Or, we could make the loss of the learning-machine reach perfectly zero, thereby representing a perfect solution[^7]. Hard problems like recognizing faces are approximately solved as a consequence of a single forward pass through a learning machine. If the loss could be made to reach zero, then we could consider the problem to be perfectly solved. Solvability can then happen in a feed-forward phase, which for practical purposes appears to be polynomial.[^8]
+
+Next, we redirect the reader's attention to neumann's theory of self-reproducing automaton[@neumann1966theory]. His idea of a self-replicating colony was that there is an infinite source of resources a.ka. reservoir which is shared by the automaton which operate at different locations of a colony. The colony uses up the shared resources, does self-replication and in this way converts raw materials/matter into useful intelligent-behaviour. The infinite reservoir of machines he talks about then reduces to the trigger column $T$ in APM: since features are sampled from a same space, they automatically become aware of themselves, thereby making explicit attention unnecessary. This also then is same as how latents have been classically sampled in the generative models[@goodfellow2020generative]. One might argue that multiple automaton although starting their lives at the same point will need to communicate among themselves, as they differ among their configurations at later point in their lifespans[^9]. Fluctuations in $T$ are then akin to mutations. We compensate for this fact by weight-sharing the MLP across different locations in APM.
+
+**A cosmological analogy:** In physics, one of the famous theories of the origin of universe has been starting from a single point, and undergoing a continuous expansion[@hubble1929evolution]. There are alternate theories for eg, Conformal Cyclic Cosmology[@penrose2006before] which hypothesize the universe undergoing periodic cycles of expansion and contraction[@prabhupada1972bhagavad]. Drawing inspiration from these fundamental insights, the trigger column $T$ undergoes these cycles of folding and unfolding during the learning iterations.
+
+APM is inspired from GLOM's philosophy. This section explains the principles behind island of agreement in more detail to facilitate an easier understanding. GLOM [@hinton2023represent] assumes that each pixel of an input image contains a higher dimensional column vector over it. Therefore, for an image of $h \times w$, there is a column vector at every location. Next, all these column vectors undergo some sort of message passing between themselves, for eg, via attention. At the end of these procedure, the idea is that the vectors belonging to identical objects should start pointing in the same directions in the higher dimensional embedding space. A cluster of such higher dimensional vectors is then called islands of agreement. To see such islands in practice, one can then apply a dimensionality reduction procedure like t-sne to 3 dimensions, and visualize the obtained feature map by backprojecting the obtained features to the RGB range of \[0,255\][@modi2023occlusions]. Note that t-sNE preserves the higher dimensional spatial structure among the vectors since it fits gaussians. This visualization mechanism does not require any feed-forwarding or backpropogation through the net: the islands are already there, and clustering merely helps them to reveal their presence[^10].
+
+We present the islands of agreement which can be observed now in APM in Fig [1](#fig:cifar_1){reference-type="ref" reference="fig:cifar_1"},[3](#fig:cifar_2){reference-type="ref" reference="fig:cifar_2"},[4](#fig:cifar_3){reference-type="ref" reference="fig:cifar_3"}. Notice how the part-wholes in the images are clearly observable in different colours. Finally, we scaled up our APM and trained it on COCO train set. Fig [6](#fig:coco_1){reference-type="ref" reference="fig:coco_1"},[5](#fig:coco_2){reference-type="ref" reference="fig:coco_2"},[2](#fig:coco_3){reference-type="ref" reference="fig:coco_3"},[7](#fig:coco_4){reference-type="ref" reference="fig:coco_4"} visualizes the islands of agreement on COCO val set. It can clearly be seen that the net develops a semantic clustering and shows the potential to do a dense task. APM offers a unique advantage over parallel perception (DINOv2): features at a particular location can be queried *serially*. One can 'selectively' query the locations which correspond to a particular object[@yu2021unsupervised], instead of bringing the whole feature grid in existence and then choosing the relevant objects[@carion2020end]. Such an inductive bias of choosing which of the input rays/columns in a neural field corresponds to which object has already been explored by Yu et al[@yu2021unsupervised].
+
+In the presented islands of agreement, one can see that the parts and wholes of the object are all entangled into one image. In an idealistic scenario, the net should learn to map the whole \ triplet[@hinton2023represent]. GLOM looks at the features predicted by different layers of a transformer in a different way: lower layers are predicting object parts, and higher layers are predict the full object. For now, APM has *only modelled* the last layer of the transformer. In an idealistic scenario, we want the net to traverse the whole up-down part-whole hierarchy as well as learn the pose-transformation matrix which can get us from one part to its whole[@hinton2023represent].
+
+Let us next consider a 2D image of a man[@collodi1968adventures] gazing at the ground in front of him. At the object level it does not make sense for embeddings of the nose to jump to the embeddings of the ground. However, it is okay to learn pose-transformation matrices which can get us from one point on the man's body to another. This means that this restricted movement has to be informed by a top-down feedback which we have not yet modelled. Furthermore, this pose prediction can happen by a big fat-net like APM[^11] shared across locations and levels of the part whole hierarchy. This is different from capsules, which contain only a few convolution filters at a particular location. In computer graphics, the pose matrix is defined a four by four matrix where the first three by three elements are rotation components, last row are homogenous coordinates, and first three numbers in the last column are camera translations. If the net predicts a $4 \times 4$ matrix each of whose element can be any constrained number, we lose the ability to make interpretations about what the predicted matrix actually represents since it is no longer in a well defined world-coordinate frame. This problem is same as how the 'learnt' camera poses are aligned to 'real' camera poses in neural fields while doing bundle adjustment[@lin2021barf].
+
+Note that islands of agreement operate at the finest spatial granularity: there is one column vector for each pixel of the input percept. However, in the case of perceptual overcrowding [@hinton2018matrix], there might be some hidden part of an object whose inference might be made seeing the visible pixels around it. However, the island in our case does not model this, since agreement is only established for one object at a particular location [@modi2023occlusions][^12].
+
+
+
+Cifar 10 islands: Notice how individual parts are clearly observed in APM features. These features are used for downstream classification. We leverage the visualization mechanism by . These islands are not been hand-picked.
+
+
+
+
+Cifar 10 islands: Notice how individual parts are clearly observed in APM features. These features are used for downstream classification.We leverage the visualization mechanism by . These islands are not been hand-picked.
+
+
+
+
+Qualitative Results on COCO Val set: Our APM is trained on COCO-train set, and islands on COCO-val set are visualized. Note that these islands are a consequence of single feed-forward through the net and not an iterative routing mechanism as in . DINOv2 does parallel perception: i.e. all tokens are kept in the memory. However, APM does asynchronous perception: it can predict the column vector at any location asynchronously. The error map shows the error between the grid predicted by Dinov2 and the grid predicted by APM. It is mostly black which shows APM closely approximates Dinov2 grid as well as can be memory efficient. The islands shown in this figure are not hand-picked.
+
+
+
+
+Qualitative Results on COCO Val set: Our APM is trained on COCO-train set, and islands on COCO-val set are visualized. Note that these islands are a consequence of single feed-forward through the net and not an iterative routing mechanism as in . DINOv2 does parallel perception: i.e. all tokens are kept in the memory. However, APM does asynchronous perception: it can predict the column vector at any location asynchronously. The error map shows the error between the grid predicted by Dinov2 and the grid predicted by APM. It is mostly black which shows APM closely approximates Dinov2 grid as well as can be memory efficient. The islands shown in this figure are not hand-picked.
+
+
+
+
+Qualitative Results on COCO Val set: Our APM is trained on COCO-train set, and islands on COCO-val set are visualized. Note that these islands are a consequence of single feed-forward through the net and not an iterative routing mechanism as in . DINOv2 does parallel perception: i.e. all tokens are kept in the memory. However, APM does asynchronous perception: it can predict the column vector at any location asynchronously. The error map shows the error between the grid predicted by Dinov2 and the grid predicted by APM. It is mostly black which shows APM closely approximates Dinov2 grid as well as can be memory efficient. The islands shown in this figure are not hand-picked.
+
+
+1. **Claims**
+
+2. Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
+
+3. Answer:
+
+4. Justification: The first claim is that we improve on ttt's computational performance, second claim is that APM performs better than existing baselines, third claim is that APM's architecture is a single CNN filter and a MLP of 5 layers. Fourth claim, to the best of our knowledge is that we are providing the first evidence of GLOM's insight: i.e. percept is really a field . The first claim of computational complexity is analyzed in Tab [\[tab:compute_analysis\]](#tab:compute_analysis){reference-type="ref" reference="tab:compute_analysis"} and Fig [\[fig:memory_gsraph\]](#fig:memory_gsraph){reference-type="ref" reference="fig:memory_gsraph"}. Second claim of improved performance over ttt methods is present in tables [\[tab:imagenet_splits\]](#tab:imagenet_splits){reference-type="ref" reference="tab:imagenet_splits"}[\[tab:fine-grained\]](#tab:fine-grained){reference-type="ref" reference="tab:fine-grained"}[\[tab:imagenet_c\]](#tab:imagenet_c){reference-type="ref" reference="tab:imagenet_c"}.Third claim of APM's architecture will be evident in the codebase shared with this manuscript. Final claim confirming GLOM's insight i.e. percept really is a field is evident in Fig[\[fig:interpolation\]](#fig:interpolation){reference-type="ref" reference="fig:interpolation"}. A kind reader can also interpolate any two images in the wild using the codebase we shall share with this manuscript in the review process.
+
+5. Guidelines:
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+32. Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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+33. Answer:
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+34. Justification: We follow the standard practice followed in test-time-training literature [@tpt; @ttt_mae; @ttt_sun]. Furthermore, all the experiments are run with the same seed, thereby ensuring reproducibility. All the nets are initialized with same random weight matrices to help ensure experimental consistency.
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+38. Answer:
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+39. Justification: There is a whole discussion on computational complexity. ll experiments are run on a same desktop-workstation containing 1x rtx a6000/96GB ram/Ubuntu 22.04/2TB ssd.
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+49. Justification: We have added a broader impact section in the supplementary.
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+
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+51. **Safeguards**
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+52. Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
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+53. Answer:
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+54. Justification: we have used open sourced data. so there are no asset issues. our own model weights shall be released later. The presented model APM is a very small model, with potential for going into a toaster for less than a dollar, thereby making ai more accessible and useful in lives of every day people.
+
+55. Guidelines:
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+
+58. Answer:
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+59. Justification: yes, APM is inspired from GLOM whose reference we have added.
+
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+73. Answer:
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+74. Justification: The worker was a graduate student who was paid 20 hours. But he worked 90 hours because he loved it ehh.
+
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+ - Wait for it, says Barney. Now is the time for post-credits. The fun is not yet over. This message has been subtly embedded into this list, because apparently noone scrolls down till the bottom. Since you stayed with us, big kudos to you. The rise of mortal machines shall now begin lolzy. We shall see each other on the other end. Hiya hiya hiya. All credits go to shinchan, pikachu and big godzillas. But who are they?. That's a teeny-tiny secret. We might whisper it to you someday. Apparently walls also have ears.
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+
+[^1]: Correspondence to [`rajatmodi62@gmail.com`](mailto:rajatmodi62@gmail.com).
+
+[^2]: It can be decentralized.
+
+[^3]: They are not always sleeping, they sometimes wake up too.[@hinton1995wake].
+
+[^4]: Averaging is similar to pooling in convolutional-nets and might lose important information over time. It might be helpful to explore temporally-weighed averages or alternate type of long-term memory-banks[@wu2022defense].
+
+[^5]: With a point on the wavefront being its own source. But the envelope is still a growing sphere.
+
+[^6]: Interaction is only between local neighbours, yet order appears. Local-interaction is a form of local-routing, and somehow *stable* patterns appear *without* a global constraint. Unfortunately, backpropogation **still relies** on a global constraint. However, that symmetry was recently broken[@lowe2019putting]. Perhaps, the answer then lies in how the starting point instructs the unfolding process[@hiesinger2021self].
+
+[^7]: If the degree of freedom of the machine $\geq$ than the freedom needed to solve a given problem, the machine just keeps interpolating in the subspace among multiple set of weights that give the same answer[@hinton1987horizontal]. This wastes computation even if the solution has been reached. Annealing, early-stopping and quantizing weights have been some old ideas to reduce this degree of freedom. We would then need a mechanism to *dynamically adjust* the degree of freedom during inference.
+
+[^8]: A computing machine which is exponentially fast can pass on the *illusion* that a non-polynomial solution-space is now polynomial, for the time is measured in a constant observer-space[@einstein1905electrodynamics]. However, feed-forward loses *how* the solution was reached, i.e. we can no longer predict precise algorithmic steps. Mathematical-imprecision could still lead to a perfect solution. An extreme loss of precision means that the net could then run in an analog hardware[@hinton2022forward]
+
+[^9]: As a consequence of inherent markov-stochasticity. Biologically, Darwin called it evolution[@darwin2016origin]. In machines, it can be inbuilt randomness in a computing element[@turing1936computable; @turing1990chemical; @turing2009computing].
+
+[^10]: In this material nature, the boundaries around objects are not precise boxes. Rather they gradually blend in the background. This is known as sfmato effect which leonardo hath incorporated whilst painting Mona Lisa[@modi2020detection]. Similarly, GLOM proposes to do away with boxes altogether[@hinton2023represent; @modi2023occlusions].
+
+[^11]: A big-fat net being used to predict embedding at each location in the part-whole hierarchy is *different* from simply making only the embeddings compete among themselves[@garau2022interpretable]. Replicating the same net across locations is costlier than replicating location-specific input-queries to a shared net. Both achieve the same effect. Of course, our weight sharing is not biologically plausible, which also remains a deficiency of GLOM[@hinton2023represent].
+
+[^12]: We could use non-intuitive hungarian-matching to resolve order among predictions of multiple object in the scene[@modi2023occlusions; @carion2020end]. It is non-intuitive, for the objects in this material-world *don't* permute in reality, and *dont* choose the combination which shall result in the lowest loss. Rather the brain surfs reality[@hinton2023represent]. The brain then is the neural net, and the objects are the external input from the environment[@hinton1984boltzmann]. The objects/islands are already there, and reveal themselves when fixated upon. If not, they then just collapse into a superimposed quantum state, for a tilted cube or a diamond are nothing but a *same* object[@penrose2017consciousness]. The granularity of fixation can then vary in practice,aka saccades. Quantum state can then arise in a digital machine as a consequence of kolmogorov's theoram. Such effects can then happen at room temperature instead of super-cooled matter states[@einstein2005quantentheorie]. The only difference is that a system of parallel-connected components requires gradient descent in a digital machine. In a quantum machine, there can be a different mechanism for *relaxation*.
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+# Introduction
+
+The recent advancements in large language models (LLMs) based on transformer architectures (Vaswani et al., 2017; Brown et al., 2020) have impressive empirical performance in various tasks. These transformer models with millions to billions of parameters, pose a formidable challenge in comprehending the intricate processes transpiring within their layers. Initial efforts have been undertaken to unravel mechanistic interpretability (Elhage et al., 2021) and adopt a mathematical approach (Geshkovski et al., 2023a;b) to better understand the inner workings of these complex models.
+
+As the field of LLMs continues to evolve and grow, it is important to develop a fundamental approach to understanding such complex models. This can provide stepping stones for improving trustworthiness, interpretability, and alignment in LLMs (Shevlane et al., 2023). To this aim, there are multifaceted approaches combining insights from mechanistic interpretability, mathematical analysis, and other complementary methods. Rigorous approaches from Geshkovski et al. (2023a;b) open
+
+∗Qualcomm Vietnam Company Limited
+
+interesting directions, providing theoretical understanding of transformers using neural ordinary differential equations (neural ODEs) (Chen et al., 2018). In practical applications, neural ODEs have been used in models proposed by Lu et al. (2019); Zhong et al. (2022); Dutta et al. (2021). While Geshkovski et al. (2023a;b) suggest that the model outputs collapse to a small number of points at infinite depth, Zhong et al. (2022); Dutta et al. (2021) demonstrate the use cases of neural ODE-based transformers. However, the main limitation in this line of work is that they often rely on shared-weight assumptions, which leads to a discrepancy between theoretical results and practical observations. Theoretically, the clustering behavior of fixed points in these studies (Geshkovski et al., 2023a;b) does not align well with the outputs of autoregressive transformers, which flexibly predict next tokens rather than forming clusters. Practically, the potential of differential equation-like adaptive computation is often overlooked, as evidenced by the limited exploration of neural ODE versions of transformers with time-dependent vector fields, as opposed to weight-sharing approaches.
+
+In this paper, we introduce a novel approach to modeling transformer architectures using highly flexible non-autonomous neural ODEs1. Apart from existing methodologies that seek weight-sharing, our proposed model generates all the weights of attention and feed-forward blocks through hyperneural networks. Specifically, we consider weights as functions of a continuous layer index (or time). We conduct a comprehensive exploration and analysis of its dynamics through the lens of dynamical systems. Our primary analytical tool is spectral analysis that examines the components of our models including attention blocks and feed-forward blocks. Our analysis reveals some important properties of our models. The observed spectral dynamics tend to exhibit increasing magnitudes of eigenvalues. Through simulation, we demonstrate that this phenomenon inhibits the emergence of clusters, contrasting with the results reported in Geshkovski et al. (2023a). In other aspects, we use the Lyapunov exponent to study token-level sensitivity, which can be a potential tool for explainable machine learning methods (Gunning et al., 2019).
+
+Furthermore, we present a novel approach that leverages the adaptive computation of differential equations to create a versatile pretrained model. Our model exhibits a unique characteristic: the ability to be fine-tuned under various architectures derived from the initial pretrained structure. When presented with novel datasets, our model dynamically adjusts the step size of its ODE solvers. This adaptation effectively transform the model's architecture, allowing it to function as a vanilla transformer. Consequently, this enables the application of a wide range of fine-tuning techniques such as LoRA (Hu et al., 2021). To the best of our knowledge, this represents the first instance of a pretrained model capable of being fine-tuned across diverse architectural configurations.
+
+The contributions of this paper are threefold. First, we introduce neural ODE transformers having comparable performance with GPT in different settings and data sets. Second, we offer a comprehensive analysis of the proposed models, unraveling their intricate internal dynamics and a technique to assess token-level sensitivity, enhancing our ability to interpret model behaviors. Lastly, we show that our model provides flexible computation fine-tune while maintaining a competitive performance.
+
+Transformers proposed by Vaswani et al. (2017) consist of key components, including self-attention and feed-forward neural network.
+
+**Attention mechanism** Consider a sequence with n tokens, $X_1, \ldots, X_n$ in $\mathbb{R}^d$ , and represent it as a matrix $X \in \mathbb{R}^{n \times d}$ . Let us define three linear projections $Q, K, V \in \mathbb{R}^{d_{\text{atm}} \times d}$ for query, key, and value, respectively. The self-attention operation is expressed as follows:
+
+$$\operatorname{Attn}(Q,K,V;X) = \operatorname{softmax}\left(\frac{XQ^{\top}KX^{\top}}{\sqrt{d}}\right)(XV^{\top}). \tag{1}$$
+
+In essence, the self-attention mechanism acts as an operator determining where to focus attention within the entire sequence. The softmax function introduces emphasis, making it more likely that one token will be highlighted over others.
+
+<sup>1Non-autonomous ODEs have time-dependent vector fields.
+
+Self-attention is further extended into multi-head attention. In this configuration, multiple independent self-attention outputs, referred to as *heads*, are concatenated and reprojected, allowing for the introduction of residual connections. The formal definition is given by:
+
+MultiHead :=Concat(head1,...,headH)
+$$O$$
+,
+where headi :=Attn( $Q_i, K_i, V_i; X$ ), $i = 1,..., H$ .
+
+Here, H is the number of heads and $O \in \mathbb{R}^{d_{\text{concat}} \times d}$ is a projection matrix.
+
+**Feed-forward block** The second crucial component is the feed-forward layer, modeled as a two-layer multilayer perceptron with a nonlinear activation function, expressed as:
+
+$$\operatorname{GeLU}(XW_1^{\top} + b_1)W_2^{\top} + b_2,$$
+
+where $W_1 \in \mathbb{R}^{d_{\text{mlp}} \times d}$ , $W_2 \in \mathbb{R}^{d \times d_{\text{mlp}}}$ , $b_1 \in \mathbb{R}^{d_{\text{mlp}}}$ , and $b_2 \in \mathbb{R}^d$ . The feed-forward layer, a singular component in the transformer, introduces nonlinearity in the computational flow, enhancing the model ability to capture complex patterns and relationships.
+
+# Method
+
+We conduct additional experiments to evaluate the adaptability of DIFFEQFORMER across different architectures. Specifically, we implement a LLaMA-based version of DIFFEQFORMER following the architecture proposed by Touvron et al. (2023). The key architectural differences between LLaMA-based and GPT-based implementations include: (1) the use of rotary positional embeddings (RoPE) instead of absolute positional embeddings, (2) RMSNorm for layer normalization rather than the standard LayerNorm, and (3) SwiGLU activation functions in feed-forward blocks instead of ReLU. Importantly, these architectural modifications do not alter the core principles and effectiveness of DIFFEQFORMER's design.
+
+
+
+Figure 8: Comparison of validation perplexity between our model and Llama-1B, both trained with Adam optimizer ( $\beta_1 = 0.9, \beta_2 = 0.95$ ).
+
+Figure 8 compares the validation perplexity between our Llama-based DIFFEQFORMER and the baseline Llama-1B on the OPENWEBTEXT dataset. Our model consistently achieves lower perplexity, demonstrating DIFFEQFORMER's effectiveness when implemented with modern architectures like Llama.
+
+We evaluate the performance of DIFFEQFORMER and baselines in the setting of GPT-large and Llama-1B. We use the framework lm-evaluation-harness (Gao et al., 2024) to evaluation the pre-trained models. The performance is assessed with the tasks having the similar setup with Biderman et al. (2023). Figure 9, Figure 10, and Table 4 show the performance compared against the GPT-large baseline across different tasks in two settings: zero-shot and five-shot. Figure 11, Figure 12, and Table 5 show the performance compared against the Llama-1B baseline across different tasks in two settings: zero-shot and five-shot.
+
+**GPT-large** Our model demonstrates competitive performance against GPT-large across multiple benchmarks. In zero-shot evaluations (Figure 9), we achieve statistically significant improvements on reading comprehension tasks: Lambada OpenAI and Lambada Standard. Additionally, we observe modest but consistent gains on reasoning tasks including LogiQA, PIQA, and SciQ. In five-shot settings (Figure 10), our model outperforms GPT-large on 5 out of 10 tasks, with statistically significant improvements on Lambada Standard and Winogrande. Performance remains comparable on two tasks, while GPT-large shows marginal, though not statistically significant, advantages on two others. These results suggest our neural ODE architecture maintains or exceeds GPT-large's capabilities across diverse language understanding and reasoning tasks.
+
+**Llama-1B** Our model demonstrates notable improvements over Llama-1B in both zero-shot and five-shot settings, as shown in Figures 11 and 12. The improvements are particularly pronounced in
+
+reading comprehension tasks - we observe statistically significant accuracy gains (2% - 4%) on both Lambada OpenAI and Lambada Standard benchmarks. These results suggest our neural ODE-based transformer architecture enhances language understanding capabilities while maintaining competitive performance across other evaluation metrics.
+
+
+
+Figure 9: Benchmark score for zero-shot in GPT-large configuration.
+
+
+
+Figure 10: Benchmark score for five-shot GPT-large configuration.
+
+
+
+Figure 11: Benchmark score for zero-shot in Llama-1B configuration.
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+# Introduction
+
+Understanding non-speech sounds, non-verbal speech, and music (collectively referred to as "audio" in this paper) is essential for real-world applications such as detecting anomalies in industrial environments, recognizing emotional cues, and improving assistive technologies for the impaired. While Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through language, extending these systems to comprehend audio is key to building intelligent systems capable of reasoning with contextual auditory cues [@kong2024audio]. Verbal speech, inherently tied to language, benefits significantly from (L)LM advancements [@watanabe2018espnet; @chen2024hyporadise]; however, the potential to enhance perception and reasoning over non-verbal audio remains largely under-explored [@ghosh-etal-2024-gama].
+
+{#fig:radar_af2 width="90%"}
+
+Audio-Language Models (ALMs) extend language models with audio understanding capabilities. Contrastive Language-Audio Pre-training (CLAP) [@elizalde2023clap] was among the first encoder-only ALMs to bridge audio and language with contrastive learning. Building on this, subsequent efforts introduced Large ALMs (LALMs), which integrate audio encoders with pre-trained decoder-based LLMs, enabling open-ended Audio Question Answering (AQA) and free-form response generation [@gong2021ast; @tang2024salmonn; @ghosh-etal-2024-gama]. However, despite these developments, even the most advanced LALMs continue to underperform on expert-level reasoning tasks compared to foundational tasks like event classification. For example, Gemini-1.5-Pro [@team2024gemini], one of the most advanced models, achieves only 54.4% and 48.5% on the MMAU sound and music subsets [@sakshi2024mmau], a benchmark for evaluating expert-level audio reasoning. This underscores the challenges of improving an LLM's ability to understand and reason over audio, which we attribute to the lack of high-quality training data and robust audio representations.
+
+**Main Contributions:** In this paper, we propose **Audio Flamingo 2** (AF2), a parameter-efficient ALM that combines a 3B-parameter small decoder LM with a 203M-parameter audio encoder, achieving state-of-the-art audio understanding and reasoning capabilities. There are three major innovations in our method: $(1)$ **Data:** Recent studies highlight that improving data quality can rival or even surpass the performance gains achieved by scaling compute and model size [@abdin2024phi]. To this end, we propose **AudioSkills** (Section [4.1](#sec:af2_audioskills){reference-type="ref" reference="sec:af2_audioskills"}), a large-scale, skill-specific AQA training dataset featuring complex, reasoning-intensive questions paired with each audio. We design questions that target *seven* distinct skills, with the primary aim of improving fine-grained reasoning capabilities in ALMs. $(2)$ **Audio Encoding:** We propose AF-CLAP (Section [3.1](#sec:af2_clap){reference-type="ref" reference="sec:af2_clap"}), where we scale CLAP training to over 8M audio-caption pairs, incorporating synthetic data, and propose an improved contrastive loss for better representational quality and robustness. $(3)$ **Training Strategy:** We propose a novel 3-stage curriculum training strategy (Section [5](#sec:af2_strat){reference-type="ref" reference="sec:af2_strat"}) for improved performance.
+
+Additionally, for the first time, we extend audio understanding to *long audios*, moving beyond 30-second clips to audios lasting up to 5 minutes. To enable the model to comprehend and reason over long audio, we introduce **LongAudio**, a novel dataset comprising over 260k carefully curated AQA instances with audios ranging from 30 seconds to 5 minutes. LongAudio spans 10+ audio categories and supports 6 tasks, including captioning and 5 reasoning-based QA tasks. Next, we introduce **LongAudioBench**, an expert-annotated benchmark for evaluating ALMs on long audio understanding. AF2, trained on LongAudio, significantly outperforms our baseline. In summary, our main contributions are:
+
+1. We present Audio Flamingo 2, a SOTA ALM with advanced audio understanding and reasoning capabilities.
+
+2. We propose innovations in data generation, architecture design, representation learning, and training strategies.
+
+3. We introduce the long audio understanding task and create dedicated training and evaluation datasets to drive progress in this area.
+
+4. Audio Flamingo 2 outperforms larger and proprietary LALMs across over 20 benchmarks, despite being smaller and trained exclusively on public datasets.
+
+5. We conduct systematic ablation studies to demonstrate the impact of each design choice.
+
+# Method
+
+Fig. [2](#fig:train_pipe){reference-type="ref" reference="fig:train_pipe"} summarizes the AF2 architecture, consisting of four primary components: *i)* AF-CLAP: a CLAP-based audio encoder with sliding window feature extraction, *ii)* audio representation transformation layers for additional capacity, *iii)* a decoder-only language model, and *iv)* gated cross-attention (XATTN-Dense) layers for audio conditioning.
+
+CLAP, pre-trained with contrastive loss on audio-caption pairs, shows strong audio understanding and natural language alignment [@Wu2022LargeScaleCL; @elizalde2023clap]. These make CLAP a suitable choice for building ALMs. However, CLAP is less favored in prior works [@tang2023salmonn; @chu2023qwen] due to its under-performance compared to SSL pre-trained audio encoders [@gong2023contrastive; @li2024mert; @ghosh-etal-2024-gama]. We hypothesize this is due to the limited availability of high-quality audio-caption pairs, which causes CLAP representations to struggle with compositional reasoning [@ghosh2024compa] and linguistic variations in captions [@selvakumar2024audio].
+
+In this section, we address these issues and introduce an improved version of CLAP called AF-CLAP. Specifically, we focus on: *i)* constructing a large-scale, high-quality training dataset, and *ii)* improving the training objective to for better representational robustness.
+
+We scale the training dataset for AF-CLAP to over 8M (10-second) audio-caption pairs. We collect these data from open-source audio and video datasets, with an emphasis on audio diversity and caption accuracy.
+
+Existing models like Laion-CLAP @Wu2022LargeScaleCL and MS-CLAP [@CLAP2023] rely heavily on single-label audio classification datasets for captions. This limits their ability to generalize to complex, real-world audio with diverse compositions. Automated captioning efforts (e.g., @yuan2024sound) yield only about 1.4M pairs, lack diversity, and under-represent critical domains like home sounds.
+
+Inspired by the recent success of training vision LMs on images from long videos [@venkataramanan2024is], we collect audio from open long-video datasets. Specifically, we select 100k diverse videos from MiraData [@ju2024miradata] and Video Recap [@10657851] (see Appendix [15.1](#subsec:app_video_topic){reference-type="ref" reference="subsec:app_video_topic"} for selection details). We segment these videos into 10-second clips and generate video captions using Qwen2-VL-2B-Instruct and audio captions using Qwen2-Audio. To ensure diversity and reduce redundancy, we filter out segments with audio-visual similarity above a threshold $p$. We then prompt GPT-4o(2024-05-13) (Prompts [23](#fig:mira_short_prompt){reference-type="ref" reference="fig:mira_short_prompt"} and [22](#fig:audio_caption_recap_prompt){reference-type="ref" reference="fig:audio_caption_recap_prompt"}) to generate audio-centric captions that exclude visual attributes and emphasize sound events. Using this approach, we collect approximately **5.5M** new audio-caption pairs, including 4M from unlabeled short audios and 1.5M from long videos. Detailed dataset statistics are provided in Table [\[tab:clap_datasets\]](#tab:clap_datasets){reference-type="ref" reference="tab:clap_datasets"}.
+
+Compared to the standard contrastive loss in audio-language pre-training [@Wu2022LargeScaleCL; @elizalde2023clap], we improve the training objective for better robustness to linguistic variations and compositional reasoning abilities.
+
+**Improving Linguistic Invariance:** CLAP-like models struggle to generalize to linguistic variations in captions that humans easily understand [@selvakumar2024audio] (e.g., failing to equate *helicopter* and *chopper*). To address this, for every caption in the dataset, we generate $M-1$ linguistically varied captions with identical semantics and composition. These variations, along with the ground-truth caption, are treated as *positives*.
+
+**Improving Compositional Reasoning:** Captions with different word orders or structures often convey distinct relationships between acoustic events, such as temporal sequencing or attribute binding. However, CLAP-like models struggle to capture these nuances (e.g., differentiating whether one sound follows or precedes another) [@ghosh2024compa]. To address this, we introduce composition-aware negatives. For every $M$ positive captions, we generate $N$ variations with modified temporal or attribute compositions and use them as *negatives*. An example is below:
+
+::: mybox
+**Original Caption:** A dog barking followed by the sound of a train approaching.
+
+**Positive:** A dog barking followed by the sound of a railcar approaching.
+
+**Negative:** A dog barking preceded by the sound of a railcar approaching.
+:::
+
+{#fig:enter-label width="\\columnwidth"}
+
+**Contrastive Loss.** Each sample $x$ in the training data $\mathcal{X}$ now has $M$ positives $\{\mathcal{P}(x)_m\}_{m=1}^M$ and $M \times N$ negatives $\{\mathcal{N}(x)_{m,n}\}_{m=1,n=1}^{M,N}$. Let $\mathcal{A}(x)$ be audio embedding from the HTSAT~large~ audio encoder [@htsatke2022] with MLP projection, and $\mathcal{T}(x)$ be the text embedding from Flan-T5 text encoder [@raffel2020exploring; @chung2024scaling] with MLP projection. Let $\texttt{s}(u,v)=\exp(u^{\top}v/\tau)$ be the similarity function with temperature $\tau$. Our training objective is: $$\mathcal{L} = -\frac{1}{B} \sum_{i=1}^{B} \log \frac{S(i,i)}{S_{\text{neg}}(i) + \sum_{j=1}^B S(j,i)},$$ where $$S(j,i) = \sum_{m} \texttt{s}(\mathcal{T}(\mathcal{P}(x_{j})_m), \mathcal{A}(x_i)),$$ $$S_{\text{neg}}(i) = \sum_{m,n} \texttt{s}(\mathcal{T}(\mathcal{N}(x_{i})_{m,n}), \mathcal{A}(x_i)),$$
+
+where $B$ is the batch size. Our approach encourages CLAP to learn both linguistic invariance and compositional reasoning, aligning its capabilities more closely with human-like understanding and thus providing more human-aligned representations. AF-CLAP achieves SOTA performance on various retrieval and audio classification benchmarks. Since comparing CLAP performance is beyond our scope, we compare results in Tables [\[tab:exp-t2a-retrieval\]](#tab:exp-t2a-retrieval){reference-type="ref" reference="tab:exp-t2a-retrieval"} and [\[tab:zshot_res\]](#tab:zshot_res){reference-type="ref" reference="tab:zshot_res"}.
+
+**Audio Feature Extraction.** For each 10-second segment, we extract dense audio features $h \in \mathbb{R}^{64 \times 2048}$ from the penultimate layer of AF-CLAP. This approach yields higher-quality features compared to mean-pooled representations from the final layer (see Appendix [9.2](#app:feature_extract){reference-type="ref" reference="app:feature_extract"}). For longer audio, we use non-overlapping sliding windows to compute and concatenate audio features. The maximum number of sliding windows varies across training stages, with up to 30 windows (5 minutes) when training on LongAudio. Once the sliding window features are obtained, we apply RoPE [@su2023roformerenhancedtransformerrotary] with a base of 4096 to encode temporal information into the features.
+
+**Representation Transformation Layers.** To expand model capacity, we apply three self-attention layers to the audio feature representations, each with 8 heads and an inner dimension of 2048 [@kong2024audio; @vaswani2017attention].
+
+**Gated Cross-Attention.** Following Audio Flamingo, we use gated cross-attention dense (XATTN-Dense) layers from Flamingo [@alayrac2022flamingo] to condition audio representations on the LLM. Each layer consists of two blocks: (1) a residual block with cross-attention and tanh gating and (2) a residual block with a dense layer and tanh gating. These layers are inserted before each LLM block.
+
+The XATTN-Dense layers reduces the quadratic attention complexity in prefix tuning to linear complexity. For instance, let $l_1=80$ be the text token length and $l_2=30\times64$ be the number of audio embeddings. Prefix tuning requires a self-attention complexity of $(l_1+l_2)^2=4\times10^6$, whereas our cross-attention complexity is around $l_1\times l_2\approx 1.5\times10^5$.
+
+**Frozen Language Model.** Our architecture uses Qwen2.5-3B [@yang2024qwen2], a decoder-only causal LLM with 3B parameters, 36 hidden layers, and 16 attention heads. We find this model to have the best cost-performance ratio, as it has enough capacity for audio understanding and is light enough (see Section [6.6](#subsec:llm_performance){reference-type="ref" reference="subsec:llm_performance"} for a comparison of LLM sizes).
+
+
+
+Examples from AudioSkills. Compared to other AQA datasets, questions in AudioSkills require deliberate reasoning.
+
+
+Table [\[tab:dataset_by_type\]](#tab:dataset_by_type){reference-type="ref" reference="tab:dataset_by_type"} summarizes the datasets used to train AF2. We first convert common benchmark datasets used in Audio Flamingo to AQA format (see Appendix [13.1](#app:prompt_found){reference-type="ref" reference="app:prompt_found"}). In addition, we propose two datasets: AudioSkills for audio expert reasoning and LongAudio for long audio understanding.
+
+Expert-level reasoning requires mastery of diverse and specialized skills [@ericsson2003acquisition; @huang2022towards]. However, most existing audio datasets focus on surface-level properties, such as acoustic events or category classification. This limitation extends to QA pairs derived from these datasets, which often fail to require expert-level reasoning.
+
+To address this gap, we introduce **AudioSkills**, a high-quality, skill-specific synthetic dataset designed to prioritize the development of reasoning and problem-solving abilities. This dataset is carefully curated to ensure diversity and relevance, grounded in the hypothesis that expert reasoning emerges from the mastery of various relevant skills and world knowledge. Specifically, we use a combination of open-source sound and music datasets and synthetic audio and prompt GPT-4o, along with any available metadata, to create QA pairs. AudioSkills focuses on audios $\leq30$ seconds long to enable effective scaling on open-source datasets. It spans *seven* distinct skills selected for their plausibility, relevance, and ablation insights as follows:
+
+1. **Temporal Reasoning:** Understanding temporal relationships between acoustic events in the audio. We generate 4 types of MCQ-type QA pairs: ***i) Temporal Order:*** Understanding the sequential order of events or sounds; ***ii) Temporal Attribute:*** Understanding how sound attributes change over time; ***iii) Temporal Referring:*** Answering questions referring to sounds at specific temporal locations in the audio (e.g., start, middle, end); ***iv) Temporal Grounding:*** Identifying the temporal location of specific acoustic events.
+
+2. **Attribute Identification:** Recognizing attributes of events in multi-event audio, such as sound characteristics (e.g., *loud* bang) or gender (*e.g., *male* speech*). We generate QA pairs using attributes extracted via the method proposed by @kumar2024sila.
+
+3. **Counting:** Counting occurrences of specific sounds in an audio. We use the Synthio TTA model [@ghosh2024synthio] to create QA pairs at 3 difficulty levels: ***i) Level 1:*** Single sounds concatenated; count the event occurrences. ***ii) Level 2:*** Multiple interleaved sounds; count the main sound occurrences. ***iii) Level 3:*** Same as Level 2 but referenced by their temporal position.
+
+4. **Contextual Sound Event Reasoning:** Identifying the purpose of a sound or acoustic event in the context of other sounds and events in the audio. This skill requires audio understanding, temporal reasoning, world knowledge, and various other skills. Inspired by CompA-R in GAMA [@ghosh-etal-2024-gama], we expand the dataset from $\approx$``{=html}200k to $\approx$``{=html}350k QA pairs.
+
+5. **Contextual Speech Event Reasoning:** Similar to Contextual Sound Event Reasoning but focused on identifying the purpose of spoken utterances in relation to other sounds or events.
+
+6. **Information Extraction:** Focuses on understanding characteristics of the audio beyond just surface-level properties, detailed content analysis, and the application of external world knowledge when necessary.
+
+7. **General Reasoning:** This category encompasses questions that do not fall into the specific skill types above but require a combination of multiple skills or unique abilities, such as identifying relationships between multiple events or interpreting complex scenarios.
+
+In total, we generate $\approx$``{=html}4.2M QA pairs. Figure [4](#fig:audioskills_ex){reference-type="ref" reference="fig:audioskills_ex"} compares AudioSkills to other open-source AQA datasets, highlighting that these datasets lack the complexity required for deliberate reasoning. While training on such QA pairs is useful for alignment [@zhou2024lima; @wolf2023fundamental], it does not equip models with the specialized skills needed to handle complex questions [@sakshi2024mmau; @ghosh2024closer]. *Additional details, including statistics, metadata, and prompts, are provided in Appendix [11.1](#subsec:app_skills_stats){reference-type="ref" reference="subsec:app_skills_stats"} and Table [\[tab:as_cat_tab\]](#tab:as_cat_tab){reference-type="ref" reference="tab:as_cat_tab"}.*
+
+
+
+The proportion of video categories (for sourcing audios) (left), question categories (middle), and distribution of durations (right) for the LongAudio dataset with 262,928 unique AQA and 80k unique audios. We target captioning and reasoning tasks.
+
+
+
+
+The pipeline for generating LongAudio. The process begins by segmenting the long video into short video and audio clips, each ≈10 seconds. These clips are individually annotated with captions. Subsequently, an LLM is employed to generate question-and-answer pairs based on the captions of these clips. A subset of the data goes through expert review to construct LongAudioBench.
+
+
+We construct **LongAudio**, the first large-scale long audio understanding dataset, which is comprised of over 80K unique audios and approximately 263K AQA pairs. Audios are sourced from open long-video datasets, including a subset of MiraData[@ju2024miradata] (featuring diverse content from natural scenes to gaming) and the entire Video ReCap[@10657851] (egocentric videos of daily activities). To ensure diversity, we filter MiraData by clustering videos and carefully selecting samples from each cluster (see Appendix [15.1](#subsec:app_video_topic){reference-type="ref" reference="subsec:app_video_topic"}). Fig. [5](#fig:af2_charts){reference-type="ref" reference="fig:af2_charts"} (left) categorizes these videos and topics across several domains. We generate captions for video and audio segments using Qwen2-VL-2B-Instruct and Qwen2-Audio, respectively. GPT-4o is then used to generate questions that ask for captions, challenge models to reason, or extract information from long audios. Fig. [6](#fig:long_qa_pipeline){reference-type="ref" reference="fig:long_qa_pipeline"} outlines the LongAudio creation process. Fig.[5](#fig:af2_charts){reference-type="ref" reference="fig:af2_charts"} (middle) shows the distribution of these categories as outlined below:
+
+1. **Captioning:** The task is to generate detailed and accurate descriptions of long audio, focusing on capturing the essence of the entire audio.
+
+2. **Plot QA:** The task is to answer questions related to the overarching narrative or storyline of the audio, requiring an understanding of temporal and causal relationships between individual acoustic events.
+
+3. **Temporal QA:** The task is to identify the temporal positions of acoustic events and their relationships, such as order or overlap within the audio, including how certain attributes change with time.
+
+4. **Needle QA:** The task is to locate and reason about a specific "needle" segment embedded within a longer audio "haystack," ensuring the response is tied explicitly to the needle.
+
+5. **Subscene QA:** The task is to answer questions about a specific subscene within the audio, requiring identification and understanding of localized events.
+
+6. **General QA:** The task is to address broad, open-ended questions about the audio that may span multiple events, themes, or contexts, demonstrating overall comprehension and reasoning.
+
+::: table*
+:::
+
+Fig.[5](#fig:af2_charts){reference-type="ref" reference="fig:af2_charts"} (right) shows the distribution of audio lengths, and Table [\[tab:longbench_ex\]](#tab:longbench_ex){reference-type="ref" reference="tab:longbench_ex"} shows examples of each category.
+
+**LongAudioBench:** We sample 10% ($\approx$``{=html}3k) from LongAudio, proportionally across QA types, for expert human verification. Annotators manually verify and correct these instances, discarding any incorrect ones. Subsequently, three authors conduct a quality check on the corrected samples. The full annotation process is detailed in Appendix [12.7](#subsec:app_human_verification){reference-type="ref" reference="subsec:app_human_verification"}. The final version of LongAudioBench has 2,429 instances. For evaluation, following a wealth of prior work [@ghosh-etal-2024-gama; @yang2024air], we use an LLM-as-a-judge framework [@zheng2023judging]: we prompt GPT-4o with the model response and ground truth answer using prompt [25](#fig:evaluator_prompt){reference-type="ref" reference="fig:evaluator_prompt"} and score the response on a scale of 1 to 10.
+
+We train AF2 using a 3-stage curriculum learning strategy, progressively increasing the audio context length and improving data quality: ***Stage 1: Pre-training.*** This stage focuses on multi-modal alignment to align audio representations with the LLM. For training, we leverage large-scale and plausibly noisy classification and captioning datasets. Inspired by [@cheng2024instruction], we include a small amount of high-quality QA data to improve pre-training. During this stage, CLAP and LLM layers are frozen, and only the audio representation transformation and gated cross-attention layers are trainable. The audio context is restricted to $w=3$ windows (30 seconds). ***Stage 2: Fine-tuning.*** This stage focuses on improving audio understanding and reasoning by learning skills. For training, we use high-quality short-audio classification, captioning, and QA datasets. This stage is often referred to as *instruction-tuning* [@chu2024qwen2; @ghosh-etal-2024-gama]. During this stage, only the LLM layers are frozen, and the CLAP model, audio representation transformation layers, and gated cross-attention layers are trainable. The audio context length is increased to $w=9$ windows (1.5 minutes). ***Stage 3: Long Fine-tuning.*** This stage focuses on context-length extension and teaching skills specific to enabling reasoning on long audios. For training, we employ our proposed LongAudio dataset, keep the audio representation transformation and gated cross-attention layers trainable, and increase $w=30$ windows (5 minutes).
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+# Introduction
+
+Deep neural networks have attracted significant attention due to their versatility in various applications (He et al., 2016; Li et al., 2021; Wang et al., 2023). Behind these successes, initialization methods play a crucial role in promoting stable and fast-convergent training processes for networks (Sutskever et al., 2013; Arpit et al., 2019; Pan et al., 2022; 2024). Usually, initialization methods make effects by controlling the magnitude of signals. For example, Xavier (Glorot & Bengio, 2010) initialization is originally proposed to maintain signals in the non-saturated region of the sigmoid activation function by restricting signal variances, which greatly solved the difficulty of training. Then, Poole et al. (2016) propose to initialize network weights by constraining signals on the edge of chaos through dynamical isometry, which can further benefit the network training. Later, Hardt & Ma (2017) analyzed the optimization landscape of linear residual networks, and found that weights that transit identity in layers can help networks converge fast as their F-norm is close to that of the final converged weights. And identity transition also corresponds to isometry theory (Zhang et al., 2019), thereby, contributing to avoiding gradient explosion and diffusion.
+
+An instance of preserving identity across neural network layers, known as "identity-control," is depicted in Figure 1 and formally expressed as Y=X. This type of initialization can be implemented by setting specific weights (e.g., $W_2$ ) to $\mathbf{0}$ , thereby ensuring zero output in the sub-stem, as elucidated by Hardt & Ma (2017). This approach, however, poses challenges in configuring the remaining
+
+
+
+Figure 1: A case of identity-control initialization, which sets $W_2 = \mathbf{0}$ to satisfy Y = X.
+
+<sup>1Harbin Institute of Technology, Shenzhen 2The Chinese University of Hong Kong
+
+<sup>3The Hong Kong Polytechnic University 4MetaAI 5Fudan University
+
+<sup>6Shanghai Academy of AI for Science
+
+\*Corresponding author.
+
+
+
+Figure 2: Analyzing effect of initializing $W_1$ while $W_2=\mathbf{0}$ . The experiment uses Cifar10 and blocks in Figure 1, and more details are in Appendix C.5. (a) The initialization methods for $W_1$ in a rectangular format. Fixup: "Random"; ZerO: "Hadamard". And "Partial Identity" and "IDInit" denote padding $\mathbf{0}$ and I to an identity matrix, respectively. (b) Set $W_1 \in \mathbb{R}^{240 \times 240}$ and $W_2 \in \mathbb{R}^{240 \times 240}$ as square matrices. "Identity-1" represents a configuration where only one weight is initialized as $\mathbf{0}$ . Interestingly, while "Random" and "Hadamard" methods may outperform "Identity-1" in initial training epochs due to more network weights, they are hard to capture the inductive bias of "Identity-1", resulting in convergence difficulties. In contrast, IDInit can effectively leverage the training dynamics associated with "Identity-1". (c) Set $W_1 \in \mathbb{R}^{280 \times 240}$ and $W_2 \in \mathbb{R}^{240 \times 280}$ as rectangle matrices. "Default" means $W_1$ and $W_2$ are initialized with Xavier. However, "Default" proves ineffective for training, as it conflicts with dynamical isometry. Furthermore, even though "Partial Identity" exhibits the capability to transmit partial signals, it performs poorly due to rank constraint issues. Finally, IDInit maintains well-training conditions by padding the identity matrix.
+
+weight $W_1$ . Previous work such as Fixup (Zhang et al., 2019) and ZerO (Zhao et al., 2022) initialize $W_1$ using the Xavier and Hadamard methods, respectively. These initializations can adversely affect the inductive bias already established by setting $W_2 = 0$ , a setting beneficial for training. As evidenced in Figure 2, both Xavier and Hadamard methods cause difficulties in achieving convergence. Observing this, we propose initializing $W_1$ with an identity matrix I, which retains the inductive bias as $IW_2 \equiv W_2$ . Moreover, I also achieves dynamical isometry in the sub-stem layer as discussed by Zhao et al. (2022). Figure 2 demonstrates that using an identity matrix significantly aids in training convergence. Nonetheless, the practical application of an identity matrix faces two primary obstacles. First, an identity matrix requires square-shaped weights, a condition seldom met in practical networks. While a partial identity matrix (by padding 0 to an identity matrix) offers a workaround, it leads to rank constraints issues (Zhao et al., 2022) when the output dimension exceeds the input dimension, impairing network generalization. The second obstacle concerns the convergence capability. As Bartlett et al. (2019) pointed out, weights initialized with an identity matrix are difficult to converge to the ground truth, of which eigenvalues contain negative values. This convergence problem is important as it indicates a limited universality of applying an identity matrix as an initialization method.
+
+**IDInit.** In light of the preceding discussion, we aim to address these two major obstacles. To handle a non-square matrix, we pad a new identity matrix in adjacency to an identity matrix. We theoretically demonstrate this operation can resolve the rank constraint problem. Then, to alleviate the replica problem induced by this padding scheme, we impose a loosening condition on the padded identity-like matrix. Turning to the matter of convergence, we conduct an experiment to analyze it. Interestingly, we find that the convergence problem can be solved by adding a moment in an optimizer (e.g., the stochastic gradient descent optimizer), which is the most general setting for training neural networks. By introducing the identity-like matrix into the identity-control framework, we implement a fully identical initialization (IDInit), which ensures identity transition across both main and sub-stem layers. Moreover, we explore two additional techniques for improving the universality of IDInit and the identity-control framework:
+
+- (1) Higher-order Weights: An identity matrix is a 2-D array and it is necessary to consider an efficient method to transfer the identity matrix to a higher-order weight (e.g., a 4-D convolution). A previous strategy is to keep identity along the channel (see Sec. 3.1.3). However, this causes diversity loss in channels, which is harmful to performance. To remedy this shortage, we keep identity in patches alternatively for more diversity in channels to achieve improvement.
+- (2) Dead Neurons: As an identity-control method, IDInit sets the last layer of the sub-stem to 0 for transiting identity in the main branch. However, a dead neuron problem is possibly caused by
+
+this setting, especially for residual convolutional networks (Zhang et al., 2019; Zhao et al., 2022). Addressing this, we select some elements to a small numerical value ε to increase trainable neurons as in Figure 3.
+
+To our knowledge, IDInit is the first successful trial to maintain identity in both main- and substems by breaking the rank constraints, which promise the expressive power of IDInit. Then, we address the replica problem by adding small noise while maintaining the dynamical isometry. By further proposing modifications to CNNs and solutions to dead neuron problems, we have significantly improved accuracy of classifying Cifar10 on ResNet-20 by 3.42% and 5.89%, respectively. (see Section 4.2). Note that, although the identity matrix is used as initialization in prior work, it was only used for square matrix, e.g., Le et al. (2015) set a hidden-to-hidden layer in a recurrent neural network with an identity matrix for better performance. IDInit is novel for the consideration of non-standard situations, e.g., non-square matrix. On ImageNet, compared to the default random initialization, IDInit demonstrates superior performance, achieving an average improvement of 0.55%, and facilitates faster convergence across various settings, reducing the required training time by an average of 7.4 epochs. IDInit can accelerate the training procedure of BERT-Base, manifesting an 11.3% reduction in computational cost. Therefore, our approach yields consistently significant advantages in the training of neural networks.
+
+# Method
+
+The identity-control scheme serves as a practical initialization framework, with prior studies such as Fixup and ZerO demonstrating success within this paradigm. As depicted in Figure 3, IDInit achieves this scheme with two components: identity-preserving initialization and zero-preserving initialization, aimed at transferring identity and zero, respectively. We elaborate on the identity-preserving initialization, which involves padding identity matrices, in Section 3.1, and discuss the zero-preserving initialization, which addresses dead neurons, in Section 3.2.
+
+A standard identity matrix can naturally satisfy identity transition. However, in a non-square situation, this natural advantage is lost. To address this problem, we pad the identity matrix on an identity matrix to fit a non-square matrix. Specifically, for a fully-connected layer transformed from Eq. (1) as $x^{(i+1)} = \theta^{(i)} x^{(i)}$ , we set the weight $\theta^{(i)} \in \mathbb{R}^{D_{i+1}^{\mathbf{I}} \times D_i^{\mathbf{I}}}$ to
+
+$$\theta_{m,j}^{(i)} = \begin{cases} \tau, & \text{if } m \equiv j \pmod{D_i^{\mathbf{I}}}, \\ 0, & \text{otherwise.} \end{cases}$$
+ (3)
+
+The initialization formulated as Eq. (3) is termed as $\mathrm{IDI}_{\tau}$ , where IDI means the identical initialization function, and $\tau$ is calculated by considering the activation function, e.g., $\tau_{ReLU} = \sqrt{2}$ for the ReLU function. As shown in Figure 3, setting $\tau = 1$ can form $\mathrm{IDI}_1$ initialization.
+
+As proposed by Bartlett et al. (2019), weights initialized with an identity matrix face difficulty in converging towards the target when its eigenvalues include negative values. This implies a potential constraint on the convergence efficacy of the IDInit method. Consequently, we will delve deeper into this issue in the following discussion. According to their study, when layers in a neural network are initialized using the identity matrix, all the weight matrices of layers will be symmetric at each step of the training process. This persistent symmetry leads to the weights of layers always being the same at any training step, causing the aforementioned convergence difficulty. Interestingly, we find that this problem is mainly caused by the gradient descent (GD) which uses all the data in one batch, and employing a stochastic gradient descent (SGD) of which data in different batches
+
+can be different, can effectively break the symmetry in gradients which facilitates convergence, and incorporating momentum can further accelerate the convergence process.
+
+To elaborate on this problem, we present a training case for a single-layer network expressed as $y = \theta x$ , where $x \in \mathbb{R}^d$ represents the input, $y \in \mathbb{R}^d$ denotes the output, and $\theta \in \mathbb{R}^{d \times d}$ is the weight matrix. The weight matrix $\theta$ is initialized to the identity matrix I, denoted as $\theta^{(0)} = I$ . For our loss function, we employ the Mean Squared Error (MSE) and a learning rate denoted by $\eta$ . Consider two training pairs $\{x_1, y_1\}$ and $\{x_2, y_2\}$ sampled from the same dataset $\mathcal{D}$ . The network is initially trained with $\{x_1, y_1\}$ , and trained with $\{x_2, y_2\}$ in the next step.
+
+Being updated after two steps, the final gradient $\Delta \theta^{(1)}$ can be calculated as
+
+$$x_2 x_2^T - \eta x_1 x_1^T x_2 x_2^T + \eta y_1 x_1^T x_2 x_2^T - y_2 x_2^T.$$
+
+$$(4)$$
+
+While $x_2x_2^T$ is symmetric, $x_1x_1^Tx_2x_2^T$ , $y_1x_1^Tx_2x_2^T$ , and $y_2x_2^T$ can be asymmetric. To quantify the magnitude of the asymmetry in $\Delta\theta^{(1)}$ , let $\Omega=-\eta x_1x_1^Tx_2x_2^T+\eta y_1x_1^Tx_2x_2^T-y_2x_2^T$ denote the asymmetric component. The magnitude of the asymmetry can be calculated as $\mathbb{E}(||\Omega-\Omega^T||_F^2)$ . Assuming $x_1, x_2, y_1, y_2 \in \mathbb{R}^d$ are random vectors with entries that are i.i.d. Gaussian random variables distributed as $N(0, \sigma^2)$ , the magnitude of the asymmetry is bounded as
+
+$$4\eta^2 d^3 \sigma^8 - 4\eta^2 d^2 \sigma^8 + 2d^2 \sigma^4 \le \mathbb{E}(||\Omega - \Omega^T||_F^2) \le 6\eta^2 d^3 \sigma^8 + 3d^2 \sigma^4. \tag{5}$$
+
+As $\eta$ is usually 1e-1, and both training pairs $\{x_1,y_1\}$ and $\{x_2,y_2\}$ can be generally normalized to $\mathcal{N}\sim(0,1)$ , thereby, the symmetry of the weight can be sufficiently influenced as
+
+$$\theta^{(2)} = \theta^{(1)} - \eta \Delta \theta^{(1)}. \tag{6}$$
+
+When introducing a momentum $m^{(0)}$ initialized to $\Delta\theta^{(0)}$ , $\theta^{(2)}$ will be updated as
+
+$$m^{(1)} = \gamma m^{(0)} + \eta \Delta \theta^{(1)},$$
+
+
+$$\theta^{(2)} = \theta^{(1)} - m^{(1)} = \theta^{(1)} - \gamma m^{(0)} - \eta \Delta \theta^{(1)},$$
+(7)
+
+where $\gamma$ is the coefficient of m. Therefore, momentum can promote the weight to become asymmetric by accumulating the asymmetry of gradients in steps and impact more when samples are increased. We show that SGD with momentum can effectively resolve the issue of layers being the same in networks initialized with the identity matrix during training, which facilitates the convergence process. The completed derivation is provided in Sec. A.2 of the appendix.
+
+As for networks of multiple layers, when their layers are asymmetric, each layer can be updated differently which breaks the convergence problem caused by the same gradients in each step (which is stated in Lemma 5 of Bartlett et al. (2019)). As illustrated in Figure 10 of the appendix, it is evident that layers trained using SGD are different from each other, with the momentum component amplifying the degree of this difference.
+
+ZerO (Zhao et al., 2022) identifies that a dimension-increasing matrix may face a rank constraint problem if padding zero values. In this analysis, we investigate whether padding with an identity matrix results in this constraint.
+
+Rank Constraint Problem. Consider a 3-layer network with weights $\{\theta^{(i)}\}_{i=0}^2$ , where $\theta^{(0)} \in \mathbb{R}^{D_h \times D_0}$ , $\theta^{(1)} \in \mathbb{R}^{D_h \times D_h}$ , $\theta^{(2)} \in \mathbb{R}^{D_L \times D_h}$ where $D_h > D_0, D_L$ . Given an input batch $x^{(0)} \in \mathbb{R}^{D_0 \times N}$ with a size N, the formulation of the i-th layer is $x^{(i+1)} = \theta^{(i)} x^{(i)}$ , where $i \in$ [2]. Define residual component $\Delta \theta^{(1)} = \theta^{(1)}$ I. When initializing the dimension-increasing
+
+
+
+Figure 4: Two padding schemes and their influence on ranks of a layer. We trained a 3-layer network on MNIST, and set $D_0 = 768$ and $D_h = 2048$ . We plot $\operatorname{rank}(\Delta\theta^{(1)}) \in \mathbb{R}^{D_h \times D_h}$ in (b). As shown in (b), padding identity can achieve more than a rank of 768 like Hadamard, while padding zero is limited under 768. The loose condition can lead to better rank performance, however, cannot solve the rank constraint problem of padding zero. weight $\theta^{(0)}$ by padding zeros (PZ) values, the rank constraint problem refers to
+
+$$\operatorname{rank}(\Delta\theta^{(1)}) \le D_0. \tag{8}$$
+
+This rank constraint issue signifies a performance limitation associated with the initialization method. Intriguingly, our findings indicate that the initialization method $\mathrm{IDI}_{\tau}$ successfully avoids this rank constraint, as detailed in Theorem 3.1. The proof is deferred to Appendix A.4.
+
+**Theorem 3.1.** If initializing all weights $\{\theta^{(i)}\}_{i=0}^2$ by $\mathrm{IDI}_1$ , the rank of $\Delta\theta^{(1)}$ can attain
+
+$$rank(\Delta\theta^{(1)}) \ge D_0,\tag{9}$$
+
+which breaks the rank constraint.
+
+Notably, Theorem 3.1 suggests that an IDInit initialized network can break this constraint through SGD without the help of non-linearity like ReLU which is mentioned as necessary in the prior study (Zhao et al., 2022). Specifically, when non-linearity like ReLU is not applied, the rank of the middle weight being limited to $D_0$ only happens at the beginning. After training for several steps, an IDInit-initialized network can break this constraint.
+
+**Replica Problem.** When recurrently padding the identity matrix, the output features are still replicated. According to Blumenfeld et al. (2020), such a replica problem can be solved by adding noise to weights. Inspired by that, we loosen the identity condition to generate $\tau \sim N(\tau, \epsilon_{\tau})$ , while keeping most identity. $\epsilon_{\tau}$ is a small value and set to 1e-6 in this paper. With this loose condition, IDInit can give additional noise to output features and bring more feature diversity. Profiting from the feature diversity, IDInit therefore can increase the rank values as shown in Figure 4(b).
+
+Convolution layers are important structures in deep neural networks. Here, we will explore an initialization pattern for convolution with the identity transition. A convolution kernel is usually defined as $\mathcal{C} \in \mathbb{R}^{k \times k \times c_{in} \times c_{out}}$ , where $c_{in}$ and $c_{out}$ denote the number of channels of input and output, respectively, and k denotes convolutional kernel size. Similar to an identity matrix, Zhao et al. (2022) propose a channel-maintain convolution layer that transits identity by setting 0-filled $\mathcal{C}$ through $\mathrm{IDI}_{\tau}(\mathcal{C}_{n,n,:,:})$ , where $n \in \mathbb{N}^+$ and k = 2n + 1. As a convolutional kernel window size, k is usually an odd number. When $c_{in} = c_{out}$ , the convolution maintains the identity. When $c_{in} > c_{out}$ or $c_{in} < c_{out}$ , $\mathcal{C}$ will under-sample and over-sample on an input feature along channel respectively. Keeping identity is usually considered as an efficient way to improve model performance, however, we find that this setting can lead to a fatal performance degeneration (see Sec. 4.2).
+
+**Patch-Maintain Convolution.** Inspired by Han et al. (2020) that enhance model performance by increasing channel diversity, we propose to fuse spatial information by simply reshaping a matrix initialized with $\mathrm{IDI}_{\tau}$ . Specifically, we reshape the convolutional kernel $\mathcal{C}$ into a matrix $C \in \mathbb{R}^{c_{out} \times kkc_{in}}$ . We initialize C as
+
+$$\mathrm{IDI}_{\tau}(C)$$
+. (10)
+
+Then by reshaping C into $\mathcal{C} \in \mathbb{R}^{k \times k \times c_{in} \times c_{out}}$ , our initialization for a convolution is completed. This reshaping strategy can shift spatial features, thereby increasing feature diversity. We utilize $\mathrm{IDIC}_{\tau}$ to denote such a reshaping process. A detailed description is in Figure 11 in the Appendix.
+
+Given a residual network formulated by Eq. (1), prior identity-control initialization (Zhang et al., 2019; Zhao et al., 2022) set the last transformation in the residual stem to 0, i.e., $\theta^{(i,0)} = 0$ , thereby maintaining an identity as
+
+$$x^{(i+1)} = (I+0)x^{(i)} = x^{(i)}. (11)$$
+
+However, the setting can possibly cause dead neurons.
+
+**Dead Neuron Problem.** The dead neuron problem occurs when a neuron's weight becomes zero and receives zero gradients, rendering it incapable of updating. This issue is harmful to the training performance of models. Fixup (Zhang et al., 2019) only uses a multiplier of 1 after $\theta^{(i,0)}=0$ , thereby obtaining non-zero gradients. However, in a realistic implementation of neural networks, the multiplier of Batch Normalization can be set to 0 (Goyal et al., 2017), and down-sampling operation
+
+can also cause 0 filled features12. Under the implementations, $\theta^{(i,0)}$ always acquires gradients with 0 values, known as the dead neuron problem, which causes failed weight updating.
+
+
+
+Figure 5: The last weight in a residual block of a trained ResNet. More than half of elements in (a) are not trained, which is known as the dead neuron. By contrast, $IDIZ_{1e-6}$ successfully solves the dead neuron problem and makes all the elements in (b) trainable.
+
+Tackling this problem, we generate small values on $\theta^{(i,0)}$ to assist in training. Recall the goal of identity-control initialization that outputs 0. Therefore, we build a calculation to get the expectation and variance of outputs approaching 0. Considering two i.i.d variables, $v_1$ and $v_2$ , whose variances are $\sigma^2(v_1) = \sigma^2(v_2) = \varphi$ and means are $\mu(v_1) = \mu(v_2) = \gamma$ , the variable $v = \varepsilon(v_1 - v_2)$ have
+
+$$\begin{cases} \mu(v) = 0, \\ \sigma^2(v) = 2\varphi \varepsilon^2, \end{cases}$$
+ (12)
+
+where $\varepsilon$ is a coefficient, and $\sigma^2(v)$ will be limited to 0 when $\varepsilon$ is sufficiently small. Assuming elements of $x^{(i)}$ are i.i.d to each other, by applying subtraction on any two elements, the result has a mean of 0, and a variance related to $\varepsilon$ . We also take $\theta^{(i,0)} \in \mathbb{R}^{D^0_{i+1} \times D^0_i}$ as an instance. At first, we initialize $\theta^{(i,0)}$ with $\mathrm{IDI}_{\varepsilon}$ . Then consider two cases: (i) if $D^0_{i+1} < D^0_i$ , setting $\theta^{(i)}_{:,D^0_{i+1}+1:D^0_i}$ with
+
+ $\mathrm{IDI}_{-arepsilon}$ ; (ii) if $D^{\mathbf{0}}_{i+1} \geq D^{\mathbf{0}}_{i}$ , set $\theta^{(i)}_{m,j} = -arepsilon$ , when $m\%D^{\mathbf{0}}_{i} = j-1$ . Therefore, we can obtain a variance of 0 by setting arepsilon to a small value. This method is termed as $\mathrm{IDIZ}_{arepsilon}$ , and we illustrate some cases in Figure 3. In this paper, we set arepsilon = 1e-6 everywhere. As shown in Figure 5, $\mathrm{IDIZ}_{1e-6}$ successfully initializes the last weight in a residual block. In addition, we also transform $\mathrm{IDIZ}_{arepsilon}$ to a convolution form $\mathrm{IDIZC}_{arepsilon}$ through the patch-maintain scheme.
+
+The IDInit framework is characterized as follows: (1) **For Non-Residual Networks:** It involves directly applying IDI $\tau$ to fully-connected layers and IDIC $\tau$ to convolutional layers. (2) **For Residual Networks:** This includes two steps: (i) Implementing IDI $\tau$ and IDIC $\tau$ across all fully-connected and convolutional layers, respectively; (ii) Utilizing IDIZ $\varepsilon$ and IDIZC $\varepsilon$ for fully-connected and convolutional layers positioned at the end of residual blocks, and for the final classification layer.
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+# Introduction
+
+The ability to capture the notion of *objectness* in learned representations is considered to be a critical aspect for developing situation-aware AI systems with human-like reasoning capabilities (Schölkopf & von Kügelgen, 2022; Lake et al., 2017). Objectness can be characterised as understanding the environment from the perspective of its building blocks. These can further be divided into object-part composition (Hinton, 1979; 2022), which might be a potential reason why humans generalise across environments with few examples to learn from (Tenenbaum et al., 2011). Recent advances in object-centric representation learning (OCL) have shown great potential in segregating objects in observed scenes (Locatello et al., 2020b; Kori et al., 2023; Löwe et al., 2024). Indeed, the goal of OCL is to enable agents to learn representations of *objects* in an observed scene in the context of their environment, as opposed to learning global representations as in the case of traditional generative models such as variational auto-encoders (Kingma & Welling,
+
+*Proceedings of the* 42 nd *International Conference on Machine Learning*, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
+
+
+
+Figure 1. (a) Occlusion Ambiguity: the orange object, which is occluded by the blue object, could be any of the six plausible objects shown on the right. (b) View Ambiguity: the blue object is observed from two different viewpoints (represented with a red arrow and a dot), leading to a change in its overall shape. In general, identifiable representations resolve ambiguities by determining the most plausible object under occlusion and correct object properties in case of view transformation by leveraging information from multiple viewpoints.
+
+2013). OCL approaches enable agents to learn spatially disentangled representations, which is an important step in compositional scene generation (Bengio et al., 2013; Lake et al., 2017; Battaglia et al., 2018; Greff et al., 2020) and understanding of causal (and physical) interactions between the objects (Marcus, 2003; Gerstenberg et al., 2021; Gopnik et al., 2004).
+
+Recent progress in OCL has been limited to learning scene representations from single-viewpoints (Locatello et al., 2020b; Engelcke et al., 2021; Singh et al., 2021; Kori et al., 2023; Chang et al., 2022; Seitzer et al., 2022; Löwe et al., 2024). Although these approaches can learn meaningful object-specific representations, they encounter significant challenges stemming from spatial ambiguities such as occlusion and view ambiguities (see Fig. 1 for examples). Additionally, while it has been hypothesised that these models learn un-occluded object representations even in the case of occlusions. Learning from a single viewpoint fails to capture effective object representations, due to the presence of multiple plausibilities of partially or fully occluded objects and the effects of view transformations, as demonstrated in Fig. 1 and highlighted by the results in Fig. 2 (we will revisit these results later in section 6). Another example of spatial ambiguities can be observed in Fig. 3, where object
+
+1Department of Computing, Imperial College London, London, UK. Correspondence to: Avinash Kori .
+
+
+
+Figure 2. Identifiability across a number of views measured with Slot Mean Correlation Coefficient (SMCC).
+
+O4 in x 1 and x 2 can be interpreted as a cube, but only after considering x 3 we can conclude that being a pyramid.
+
+A handful of approaches, including MULMON(Li et al., 2020), DYMON(Li et al., 2021), OCLOC(Yuan et al., 2024), have considered multiple viewpoints for extracting object representations. Additionally, methods such as (Liu et al., 2025; Chen et al., 2021; Luo et al., 2024) effectively use NERF(Mildenhall et al., 2021) for constructing a 3D environment from multi-viewpoint images, where the occlusions are addressed by construction. Among these methods, MULMON, DYMON, and all NERF based approaches assume that the viewpoint annotations are known, which simplifies the problem of learning to disentangle object representations conditioned on viewpoint information.
+
+The problem setting in this work aligns with OCLOC, in that, our aim is to learn invariant object representations while simultaneously learning global view information with respect to an *implicit global coordinate frame*. This eliminates the requirement for paired viewpoint-image data. While OCLOC introduces an innovative approach for learning global view information independently of the scene, its primary focus is on achieving *object-consistency* unconditional to views rather than explicitly learning view-invariant object representations. Additionally, learning global unconditional view representations does not guarantee learning identifiable view/object representations, which was not studied for OCLOC. In this work, we provide a novel model, where object representations satisfy view-invariance and view representations satisfy *approximate equivariance* properties, allowing us to exploit objects' inherent geometry and semantics to establish correspondences across views.
+
+In single-view OCL, Kori et al. (2024); Brady et al. (2023); Lachapelle et al. (2023) make an effort in rigorously formalising the underpinning, explicit and implicit assumptions and provide conditions under which models result in learning identifiable slot representations, leaving out ambiguous scenarios. Unlike them, our approach resolves spatial ambiguities, provides theoretical guarantees for identifiability, and requires no viewpoint annotations. To the best of our
+
+
+
+Figure 3. The figure illustrates a scene with four objects O s = {O1, O2, O3, O4}, observed from three different viewpoints, each described with a set of clearly visible objects: O 1 = {O3, O4}, O 2 = {O1, O3, O4}, O 3 = {O1, O2, O3, O4}. The corresponding images are passed through view and content encoders, and sampled global view vector v is used to estimate transformation function Tθv given by parameters θ v predicted using a localisation network. We apply a view-specific inverse T −1 θv on respective images projecting them to an *implicit* space, which is used to learn view conditioned slot posterior corresponding to GMMs represented by q(s v | T −1θv (x v )), which are further aggregated to marginalize viewpoint information, resulting in a content posterior, also a GMM q(c | {s 1 , . . . , s V }), which is further accumulated across all samples resulting in optimal prior p(c).
+
+knowledge, this is the first work addressing explicit formalisations of assumptions and theory required for achieving *identifiable* object representations under occlusions with multi-view observational data. To this end, we make use of the spatial Gaussian mixture models(GMM) in latent distribution across viewpoints to encourage identifiability without additional auxiliary data. Our main contributions in this work can be summarised as follows:
+
+- (i) We propose a probabilistic slot attention variant, *View-Invariant Slot Attention (VISA)* for learning identifiable object-centric representations from multiple viewpoints, resolving spacial ambiguities such as occlusions and view ambiguities (Section 4).
+- (ii) We prove that our object-centric representations are identifiable in the case of partial or full occlusions without additional view information up to an equivalence relation with a mixture model specification (Section 5).
+- (iii) We provide conclusive evidence of our identifiability results, including visual verification on synthetic datasets; we also demonstrate the scalability of the proposed method on two new, carefully designed complex datasets MVMOVI-C and MVMOVI-D (Section 6).
+
+# Method
+
+**Probabilistic Slot Attention (PSA)** as introduced by Kori et al. (2024), presents a probabilistic interpretation of the slot attention algorithm (Locatello et al., 2020b). In PSA, a set of feature embeddings $\mathbf{z} \in \mathbb{R}^{N \times d}$ per input $\mathbf{x}$ is taken as input, and an iterative Expectation Maximization (EM) algorithm is applied over these embeddings. This process results in a Gaussian Mixture Model (GMM) characterized by mean ( $\boldsymbol{\mu} \in \mathbb{R}^{K \times d}$ ), variance ( $\boldsymbol{\sigma}^2 \in \mathbb{R}^{K \times d}$ ), and mixing coefficients ( $\boldsymbol{\pi} \in [0,1]^{K \times 1}$ ). In summary, PSA employs the initial mean sampled from the prior distribution and initial variance initialized with unit vector, then iteratively updates the mean based on assignment probabilities ( $A_{nk}$ ) using Eqn. 1, and adjusts the mean and variance accordingly as described in Eqn. 4, for T iterations.
+
+$$A_{nk} = \frac{\boldsymbol{\pi}(t)_k \mathcal{N}\left(\mathbf{z}_n; \boldsymbol{\mu}(t)_k, \boldsymbol{\sigma}(t)_k^2\right)}{\sum_{j=1}^K \boldsymbol{\pi}(t)_j \mathcal{N}\left(\mathbf{z}_n; \boldsymbol{\mu}(t)_k, \boldsymbol{\sigma}(t)_j^2\right)};$$
+(1)
+
+$$\hat{A}_{nk} = A_{nk} / \sum_{l=1}^{N} A_{lk}; \ \pi(t+1)_k = \sum_{n=1}^{N} A_{nk} / N;$$
+ (2)
+
+$$\boldsymbol{\mu}(t+1)_k = \sum_{n=1}^N \hat{A}_{nk} \mathbf{z}_n; \qquad (3)$$
+
+$$\sigma(t+1)_k^2 = \sum_{n=1}^N \hat{A}_{nk} \left( \mathbf{z}_n - \boldsymbol{\mu}(t+1)_k \right)^2$$
+ (4)
+
+**Identifiable representations.** A model is considered identifiable when different training iterations yield consistent latent distributions, thereby resulting in identical model parameters (Khemakhem et al., 2020a;c). In the context of a parameter space $\Theta$ and a family of mixing functions $\mathcal{F}$ , identifiability of the model on the dataset X is established if, for any $\theta_1, \theta_2 \sim \Theta$ and $f_{\theta_1}, f_{\theta_2} \sim \mathcal{F}$ , the condition $p(f_{\theta_1}^{-1}(\mathbf{x})) = p(f_{\theta_2}^{-1}(\mathbf{x}))$ holds for all $\mathbf{x} \in \mathcal{X}$ , implying $\theta_1 = \theta_2$ . However, in practical scenarios, exact equality or "strong" identifiability is often unnecessary, as establishing relationships to transformations, which can be manually recovered, proves equally effective, resulting in a notion of weak identifiability, where relationships are recovered up to an affine transformation (Khemakhem et al., 2020c; Kivva et al., 2022). Similar identifiability relations have been elucidated for OCL in (Brady et al., 2023; Lachapelle et al., 2023; Kori et al., 2024; Mansouri et al., 2023). The notion of $\sim_s$ equivalence relation is elaborated in Dfn. 3.1. **Definition 3.1.** ( $\sim_s$ equivalence (Kori et al., 2024)) Let $f_{\boldsymbol{\theta}}: \mathcal{S} \to \mathcal{X}$ denote a mapping from slot representation space S to image space X (satisfying Assumption F.4), the equivalence relation $\sim_s$ w.r.t. to parameters $\theta \in \Theta$ is defined as: $\theta_1 \sim_s \theta_2 \Leftrightarrow$
+
+$$\exists \boldsymbol{P}, \boldsymbol{H}, \mathbf{c}: f_{\boldsymbol{\theta}_1}^{-1}(\mathbf{x}; \mathbf{v}) = \boldsymbol{P}(f_{\boldsymbol{\theta}_2}^{-1}(\mathbf{x}; \mathbf{v})\boldsymbol{H} + \mathbf{a}), \forall \mathbf{x} \in \mathcal{X},$$
+
+
+
+Figure 4. Graphical model for multi-view probabilistic slot attention: For every image in a dataset a view $\mathbf{v} \in \mathbb{R}^{d_v} \sim p(\mathbf{v})$ , this view is used to compute transformation $\mathcal{T}_{\theta^v}$ . Similarly, desired number (< K) of content representations $\mathbf{c} \in \mathbb{R}^{N \times d_s}$ are sampled content distribution $p(\mathbf{c})$ . Finally, the image $\mathbf{x}$ is generated using the transformed content $\mathcal{T}_{\theta^v}(\mathbf{c})$ and view $\mathbf{v}$ .
+
+where $P \in \mathcal{P} \subseteq \{0,1\}^{K \times K}$ is a permutation matrix, $H \in \mathbb{R}^{d \times d}$ is an affine matrix, and $\mathbf{a} \in \mathbb{R}^d$ .
+
+Let $\mathbf{x}^{1:V} = {\{\mathbf{x}^1, \dots \mathbf{x}^V\}} \in \mathcal{X} = \mathcal{X}^1 \times \dots \times \mathcal{X}^V$ , V views of the same scene observed from different viewpoints with an observational space $\mathcal{X} \subseteq \mathbb{R}^{V \times H \times W \times C}$ . We consider [V] as a shorthand notation for $\{1,\ldots,V\}$ . Let $\mathcal{O}^e=\mathcal{O}^1\cup$ $\cdots \cup \mathcal{O}^V$ correspond to an abstract notion of object sets of an environment, while $\mathcal{O}^v, \forall v \in [V]$ is a set of objects present in a considered viewpoint v. Importantly, we consider that the number of objects per viewpoint can vary, i.e., $|\mathcal{O}^1 \cup$ $\cdots \cup \mathcal{O}^V | \geq |\mathcal{O}^v| \ \forall \ v \in [V]$ , allowing for partial or full occlusion in some viewpoints. Let $\mathbf{v}^{1:V} \in \mathcal{V} = \mathcal{V}^1 \times \cdots \times \mathcal{V}^N$ $\mathcal{V}^V \subseteq \mathbb{R}^{V \times d_v}$ be inferred viewpoint-specific information1, while $\mathbf{s}_{1:K}^{1:V} \in \mathcal{S} = \mathcal{S}^1 \times \cdots \times \mathcal{S}^V \subseteq \mathbb{R}^{V \times K \times d_s}$ correspond to a viewpoint-specific slot representation. Let $\mathbf{c}_{1:K} \in$ $\mathcal{C} \subseteq \mathbb{R}^{K \times d_c}$ capture the notion of an aggregate, effectively accumulating the object knowledge across viewpoints. For any subset A of [V], we represent scene observations as $\mathbf{x}^A = {\mathbf{x}^i : \forall i \in A} \in \times_{i \in A} \mathcal{X}^i$ . The inferred viewpoints and the view specific slots are denoted as $\mathbf{v}^A = \{\mathbf{v}^i:$ $\forall i \in A \} \in \mathbf{x}_{i \in A} \mathcal{V}^i$ , and $\mathbf{s}_{1:K}^A = \{\mathbf{s}_{1:K}^i : \forall i \in A \} \in A$ $\times_{i \in A} S^i$ , respectively. We define $p_A(\mathbf{c})$ as the distribution of c over A. A more comprehensive summary of notations and terminologies is provided in App. A.
+
+In modelling, w.l.o.g, we consider access to a certain subset $A \subseteq [V]$ , ensuring the model's applicability across different scenarios. Furthermore, to simplify notation, we sometimes do not include the superscript denoting the full set of views, thereby using $\mathbf{x} = \mathbf{x}^A$ , $\mathbf{s}_{1:K} = \mathbf{s}^A_{1:K}$ , and $\mathbf{v} = \mathbf{v}^A$ interchangeably. Likewise, if we do not specify the subscripts for $\mathbf{c}$ and $\mathbf{s}$ , it implies they represent the entire collection of objects, specifically as $\mathbf{s} = \mathbf{s}^A_{1:K}$ and $\mathbf{c} = \mathbf{c}_{1:K}$ . Lastly, for any function f that operates on two distinct in-
+
+puts $\mathbf{x} = f(\mathbf{z}, \mathbf{v})$ , its inverse is denoted by $\mathbf{z} = f^{-1}(\mathbf{x}; \mathbf{v})$ , which signifies the reversal of f conditioned on a variable $\mathbf{v}$ . In the rest of this section, we introduce all the components involved in our model. We also introduce assumptions, examples, and intuition wherever necessary. Considering the generative model Eqn. 5, which is overviewed in graphical model Fig. 4, any scene $\mathbf{x}$ is generated using view $\mathbf{v}$ and content $\mathbf{c}$ . Here, both $\mathbf{c}$ and $\mathbf{v}$ are latent variables learned with variational inference(Kingma & Welling, 2013).
+
+$$p(\mathbf{x}) = \iint p(\mathbf{x}^v \mid \mathcal{T}_{\theta^v}(\mathbf{c}), \mathbf{v}^v) \ p(\mathbf{c}) \ p(\mathbf{v}^v) \ d\mathbf{v}^v \ d\mathbf{c} \quad (5)$$
+
+**View model.** Given that the view property remains consistent across all objects, we treat the view as a global, imagelevel property as opposed to Yuan et al. (2024), where view is treated as an object-level property. Assuming access to a discrete set of viewpoints denoted by A, we consider prior over a view distribution to be a GMM represented by $p(\mathbf{v}) = \sum_{v=1}^{|A|} \boldsymbol{\pi}^v \mathcal{N}(\mathbf{v}; \boldsymbol{\mu}_v, \boldsymbol{\sigma}_v^2)$ . To learn the parameters of this GMM, we consider the posterior of the form $q_{\phi}(\mathbf{v} \mid \mathbf{x}^v) \ \forall \ v \in A^2$ . In both prior and posterior, we consider the covariance to be diagonal, implicitly making an ICA assumption (Khemakhem et al., 2020a). The sampled variable $\mathbf{v} \sim q_{\theta}(\mathbf{v} \mid \mathbf{x}^v)$ is used to estimate transformation parameters $\theta^v \in \mathbb{R}^{3 \times 2}$ as in Jaderberg et al. (2015) which makes an affine transformation map $\mathcal{T}_{\theta^v}$ , which is later applied on content c and on view-specific slots s. It is important to note that we use the same set of parameters $\phi$ across all viewpoints in A for inferring view information v.
+
+**Viewpoint specific slots.** As illustrated in Fig. 3 the inference of c depends on the view-specific slots s. For a considered image $\mathbf{x}^v, v \in A$ , we first apply an inverse view transformation $\mathcal{T}_{\theta^v}^{-1}$ and model the slot distribution as a spatial mixture model represented by $q(\mathbf{s}_{1:K}^A \mid \mathcal{T}_{\theta^v}^{-1}(\mathbf{x}^A))$ . The inverse transformation makes sure that the estimated object representations across all view in A are in a common implicit representation space. As this is an intermediate variable which does not show up in our generative model in Eqn. 5, we update the corresponding parameters with closed-form equations via expectation maximisation algorithm as in (Kori et al., 2024). The resulting slot posterior is a conditional GMM as described in Eqn. 6, where $\bar{\mathbf{x}}^v = \mathcal{T}_{\theta^v}^{-1}(\mathbf{x}^v)$ is a transformed inputs, $(\mu_k(\bar{\mathbf{x}}^v), \sigma_k^2(\bar{\mathbf{x}}^v), \pi_k(\bar{\mathbf{x}}^v))$ are mean, diagonal covariance, and mixing coefficients for the considered a view and object.
+
+$$q(\mathbf{s}^{v} \mid \bar{\mathbf{x}}^{v}) = \sum_{k=1}^{K} \pi_{k}(\mathbf{x}^{v}) \mathcal{N}\left(\mathbf{s}_{k}^{v}; \boldsymbol{\mu}_{k}(\bar{\mathbf{x}}^{v}), \boldsymbol{\sigma}_{k}^{2}(\bar{\mathbf{x}}^{v})\right) \quad (6)$$
+
+ $^{1}$ We abuse the terminology by considering viewpoint, lighting, object dimension, to be encoded in a representation $\mathbf{v}$ . Note that the $\mathbf{v}$ is inferred by the model.
+
+<sup>2We consider the parametric form of q to be Gaussian.
+
+Representation matching. Given the permutation equivariance property of slot representations, we use a matching function with a permutation matrix $P^v$ , $m_s: \mathcal{S}^A \to \mathcal{S}^A$ such that $m_s(\mathbf{s}^v_{1:K}) = P^v \mathbf{s}^v_{1:K}$ mapping representation axis $\mathbf{w}.\mathbf{r}.\mathbf{t} P^v$ . The permutation matrix $P^v$ is estimated by considering the slots of the first viewpoint $\mathbf{s}^1$ as a base representation, and other representations $\mathbf{s}^v \ \forall v \in A$ are matched to align with it. We utilise Hungarian matching, as illustrated in (Locatello et al., 2020b; Wang et al., 2023), to estimate this permutation matrix $P^v$ , to control the noise in the matching algorithm, we introduce view-warmup strategy, which we detail in App. G.5.
+
+Content aggregator. We consider $g: \mathcal{S} \to \mathcal{C}$ as a content aggregator function, which marginalises the effect of view conditioning. To achieve this, we consider a convex combination of all the aligned slot representations (aligned to a base representation), considering mixing coefficients $\pi_k(\mathbf{x}^v)$ (we use $\pi_k^v$ for simplicity) in Eqn. 6 as a combination weight. The convex combination accounts for potential object occlusions, which may cause objects to be absent in particular views ensuring only active representations are combined (refer to an intuition below), resulting in a content posterior $(q(\mathbf{c} \mid \mathbf{s}))$ , which is a GMM with mixing coefficients $\tilde{\pi}_k = \left(\sum_{v=1}^{|A|} \pi_k^v\right)/|A|$ and the parameters described in Eqn. 8 (w.l.o.g we consider $\mathbf{s}, \pi$ to represent aligned representations), refer to Lemma F.3, with $w_i = 1/|A| \ \forall i \in A$ . Additionally, algorithm 1 details the entire forward process.
+
+Based on illustrated example in Fig. 3, for images $\mathbf{x}^1, \mathbf{x}^2, \mathbf{x}^3$ , the resulting matched slots and mixing coefficients correspond to $\mathbf{s}^1 = \{\mathbf{s}^1_r, \mathbf{s}^1_r, \mathbf{s}^1_{\mathcal{O}_3}, \mathbf{s}^1_{\mathcal{O}_4}, \mathbf{s}^1_b\}, \mathbf{s}^2 = \{\mathbf{s}^2_{\mathcal{O}_1}, \mathbf{s}^2_{\mathcal{O}_3}, \mathbf{s}^2_{\mathcal{O}_4}, \mathbf{s}^2_b\}, \mathbf{s}^3 = \{\mathbf{s}^3_{\mathcal{O}_1}, \mathbf{s}^3_{\mathcal{O}_2}, \mathbf{s}^3_{\mathcal{O}_3}, \mathbf{s}^3_{\mathcal{O}_4}, \mathbf{s}^3_b\},$ where $\mathbf{s}^v_{\mathcal{O}_1}, \mathbf{s}^v_r$ , and $\mathbf{s}^v_r$ correspond to slot representation for object $\mathcal{O}_i$ , random slot representation and background information, respectively, with mixing coefficients $\pi^1 = \{0,0,1,1,1\}, \pi^2 = \{1,0,1,1,1\},$ and $\pi^1 = \{1,1,1,1,1\}$ . Proposed aggregation merges the slots ignoring the random slots $\mathbf{s}^v_r$ , resulting in $\mathbf{c}_{\mathcal{O}_1} = (\mathbf{s}^2_{\mathcal{O}_1} + \mathbf{s}^3_{\mathcal{O}_1})/2, \mathbf{c}_{\mathcal{O}_2} = \mathbf{s}^3_{\mathcal{O}_2}$ and so on.
+
+$$g(\mathbf{s}_{1:K}^{1:V}, \boldsymbol{\pi}^{1:V}) = \sum_{v=1}^{|A|} \frac{\boldsymbol{\pi}_{1:k}^{v}}{|A|\tilde{\boldsymbol{\pi}}_{k}^{v}} \mathbf{s}_{1:K}^{v}; \tag{7}$$
+
+$$\tilde{\boldsymbol{\mu}}_k(\mathbf{x}^A) = \sum_{v=1}^{|A|} \frac{\boldsymbol{\pi}_k^v}{|A|\tilde{\boldsymbol{\pi}}_k^v} \boldsymbol{\mu}_k(\mathbf{x}^v);$$
+
+$$\tilde{\boldsymbol{\sigma}}^{2}(\mathbf{x}^{A}) = \sum_{v=1}^{|A|} \left(\frac{\boldsymbol{\pi}_{k}^{v}}{|A|\tilde{\boldsymbol{\pi}}_{k}^{v}}\right)^{2} \boldsymbol{\sigma}_{k}^{2}(\mathbf{x}^{v}); \tag{8}$$
+
+Mixing function and training objective. We consider
+
+both additive and non-additive (ref. definition E.1) mixing functions $f_d: \mathcal{C} \times \mathcal{V}^v \to \mathcal{X}^v$ . For additive decoders, we use a spatial-broadcasting (Greff et al., 2019) and MLP decoders, and for non-additive mixing function, we use auto-regressive transformers (Vaswani et al., 2017). We use the shared decoder $f_d$ for all views and objects, modelling the conditional distribution $p(\mathbf{x}^v \mid \mathcal{T}_{\theta^v}(\mathbf{c}), \mathbf{v}^v)$ . To train our model in an end-to-end fashion, we maximise the log-likelihood of the joint $p(\mathbf{x}^A)$ , which results in the evidence lower bound (ELBO), Eqn. 9, check Lemma F.1. Here, we consider the distribution form of $p(\mathbf{x}^v \mid \mathbf{c}, \mathbf{v}^v)$ to be Gaussian with learnable mean with isotropic covariance.
+
+$$\mathbb{E}\log p(\mathbf{x} \mid \mathcal{T}_{\theta^v}(\mathbf{c}), \mathbf{v}) - \mathrm{KL}\left(q(\mathbf{v} \mid \mathbf{x}) \parallel p(\mathbf{v})\right)$$
+ (9)
+
+**Computational complexity:** VISA achieves $\mathcal{O}(\mathcal{VTNKd})$ with the added complexity of $2\mathcal{O}(\mathcal{VNd})$ for inverse and forward view point transformation given by $\mathcal{T}_{\theta}$ , while it retains the complexity per view to be $\mathcal{O}(\mathcal{TNKd})$ which is the same as slot attention and probabilistic slot attention. Additionally, the representation matching function contributes $\mathcal{O}(\mathcal{VK}^3d)$ : this term does not alter the dominant term, in the general case when $\mathcal{K} << \mathcal{N}$ . Similar to PSA, when VISA is combined with an additive decoder, the complexity of the decoder can be lowered due to the property of automatic relevance determination (ARD), eliminating the need to decode inactive slots.
+
+In this section, we leverage the properties of the proposed model to theoretically demonstrate the learning of identifiable representations under challenging spatial ambiguities. In this work, we consider our data-generating process to satisfy a viewpoint sufficiency assumption (refer to 5.1).
+
+**Assumption 5.1.** (View-point sufficiency) For any set $A \subseteq [V]$ , we consider set A to be view-point sufficient iff $|\mathcal{O}^A| = |\mathcal{O}^e|$ . This basically means that all the objects are visible across all the considered views A, even when an individual view may not contain all the objects.
+
+**Example 1.** Based on illustrated example in Figure 3, the scene is composition of four objects $\mathcal{O}^e = \{\mathcal{O}_1, \mathcal{O}_2, \mathcal{O}_3, \mathcal{O}_4\}$ , view point subset $A = [V] = \{1, 2, 3\}$ is considered to be view point sufficient since $\bigcup_{v \in A} \mathcal{O}^v = \{\mathcal{O}_3, \mathcal{O}_4\} \cup \{\mathcal{O}_1, \mathcal{O}_3, \mathcal{O}_4\} \cup \{\mathcal{O}_1, \mathcal{O}_2, \mathcal{O}_3, \mathcal{O}_4\} = \mathcal{O}^e$ .
+
+Given that we learn the parameters of our view-specific spatial GMM with closed-form updates, we do not use an explicit prior minimising KL divergence. Instead, we rely on the fact that marginalising the effect of data points from posterior (*aggregate posterior*) is an optimal prior (Hoffman & Johnson, 2016; Kori et al., 2024), resulting in
+
+ $p(\mathbf{c}) = \iint q(\mathbf{c}|\mathbf{s}^A, \mathbf{x}^A) d\mathbf{s}^A d\mathbf{x}^A$ . Given that GMMs are universal density approximates given enough components (even GMMs with diagonal covariances), the resulting aggregate posterior $q(\mathbf{c}) = p(\mathbf{c})$ is highly flexible and multi-modal. It often suffices to approximate it using a sufficiently large subset of the dataset if marginalising out the entire dataset becomes computationally restrictive.
+
+**Lemma 5.2** (Optimal Prior). For $A \in [V]$ , given the a local content distribution $q(\mathbf{c}_{1:K} \mid \mathbf{s}_{1:K}^A, \mathbf{x}^A)$ (per-scene $\mathbf{x}^A \in \{\mathbf{x}_i^A\}_{i=1}^M$ ), which can be expressed as a GMM with K components, the aggregate posterior $q(\mathbf{c})$ is obtained by marginalizing out $\mathbf{x}$ , $\mathbf{s}$ is a non-degenerate global Gaussian mixture with MK components:
+
+$$p(\mathbf{c}) = q(\mathbf{c}) = \frac{1}{M} \sum_{i=1}^{M} \sum_{k=1}^{K} \widehat{\boldsymbol{\pi}}_{ik} \mathcal{N}\left(\mathbf{c}; \widehat{\boldsymbol{\mu}}_{ik}, \widehat{\boldsymbol{\sigma}}_{ik}^{2}\right). \quad (10)$$
+
+*Proof Sketch.* The result is obtained by integrating the product of involved posterior densities $q(\mathbf{c} \mid \mathbf{s})q(\mathbf{s} \mid \mathbf{x})p(\mathbf{x})$ . Further, we verify if the mixing coefficients sum to one in the new mixture, proving the aggregate to be well-defined.
+
+With this, we show three main results: firstly, we show that aggregate content representations (c) are identifiable without supervision (up to $\sim_s$ ). Secondly, we show that these representations are invariant to the choice of viewpoints under assumption 5.1. Finally, we show that the model exhibits in an approximate view equivariance.
+
+**Theorem 5.3.** (Affine Equivalence) For any subset $A \subseteq [V]$ , such that |A| > 0, given a set of images $\mathbf{x}^A \in \mathcal{X}^A$ and a corresponding aggregate content $\mathbf{c} \in \mathcal{C}$ and a non-degenerate content posterior $q(\mathbf{c} \mid \mathbf{s}^A)$ , considering two mixing function $f_d$ , $\tilde{f}_d$ satisfying assumption F.4, with a shared image, then $\mathbf{c}$ are identifiable up to $\sim_s$ equivalence.
+
+Considering an example 1, with two perfectly trained models $f_d$ and $\tilde{f}_d$ . Resulting aggregate contents are described as $\mathbf{c} = f_d^{-1}(\mathbf{x}^A; \mathbf{v}^A) = \{\mathbf{c}_{\mathcal{O}_1}, \mathbf{c}_{\mathcal{O}_2}, \mathbf{c}_{\mathcal{O}_3}, \mathbf{c}_{\mathcal{O}_4}, \mathbf{c}_{\mathcal{O}_b}\}$ and $\tilde{\mathbf{c}} = \tilde{f}_d^{-1}(\mathbf{x}^A; \mathbf{v}^A) = \{\tilde{\mathbf{c}}_{\mathcal{O}_2}, \tilde{\mathbf{c}}_{\mathcal{O}_4}, \tilde{\mathbf{c}}_{\mathcal{O}_3}, \tilde{\mathbf{c}}_{\mathcal{O}_1}, \tilde{\mathbf{c}}_{\mathcal{O}_b}\}$ for $A = [V] = \{1, 2, 3\}$ . $\sim_s$ equivalence states that there exists a permutation matrix P which aligns the object order in $\tilde{\mathbf{c}}$ to match with $\mathbf{c}$ and there exists and invertible affine mapping $\mathbf{A}$ such that $\tilde{\mathbf{c}}_{\mathcal{O}_k} = \mathbf{A}\mathbf{c}_{\mathcal{O}_k} \forall k \in \{1, 2, 3, 4\}$ .
+
+*Proof Sketch.* To prove the following result, we follow multiple steps as described below: (i). We demonstrate the distribution $p(\mathbf{c})$ obtained as a result of lemma 5.2 is non-degenerate and a valid distribution, (ii). With the above results, we demonstrate invertibility restrictions on mixing functions, (iii). Finally, we constrain the subspace to affine, demonstrating $\sim_s$ of aggregate content $\mathbf{c}$ .
+
+**Theorem 5.4.** (Invariance of aggregate content) For any subset $A, B \subseteq [V]$ , such that |A| > 0, |B| > 0 and both A, B satisfy an assumption 5.1, we consider aggregate content to be invariant if $f_A \sim_s f_B$ for data $\mathcal{X}^A \times \mathcal{X}^B$ .
+
+Considering an example 1, with $A = \{1,3\}$ , $B = \{2,3\}$ , such that sets A, B are viewpoint sufficient. Let $f_A$ and $f_B$ , be trained models on $\mathcal{X}^A$ and $\mathcal{X}^B$ respectively. Resulting in $\mathbf{c} = f_A^{-1}(\mathbf{x}^A; \mathbf{v}^A) = \{\mathbf{c}_{\mathcal{O}_1}, \mathbf{c}_{\mathcal{O}_2}, \mathbf{c}_{\mathcal{O}_3}, \mathbf{c}_{\mathcal{O}_4}, \mathbf{c}_{\mathcal{O}_b}\}$ and $\tilde{\mathbf{c}} = \tilde{f}_B^{-1}(\mathbf{x}^B; \mathbf{v}^B) = \{\tilde{\mathbf{c}}_{\mathcal{O}_2}, \tilde{\mathbf{c}}_{\mathcal{O}_4}, \tilde{\mathbf{c}}_{\mathcal{O}_3}, \tilde{\mathbf{c}}_{\mathcal{O}_1}, \tilde{\mathbf{c}}_{\mathcal{O}_b}\}$ . Thm. 5.4 states that the representations $\mathcal{T}_{\theta^B}^{-1}(\tilde{\mathbf{c}}_{\mathcal{O}_k})$ can be mapped to $\mathcal{T}_{\theta^A}^{-1}(\mathbf{c}_{\mathcal{O}_k})$ by permuting object indices and an affine transformation.
+
+*Proof Sketch.* To prove this, we extend the proof of Thm. 5.3, and establish that there exist two inevitable affine functions $h_A, h_B$ for mixing functions $f_A, f_B : \mathcal{C} \times \mathcal{V} \to \mathcal{X}$ to map representations $\mathbf{c}$ with a given view set $\mathbf{v}^A$ to observations $\mathbf{x}^A$ . Later, we show that, in the case of invariance, an affine mapping exists from $h_A$ to $h_B$ .
+
+**Theorem 5.5.** (Approximate representational equivariance) For a given aggregate content $\mathbf{c}$ , for any two views $\mathbf{v}, \tilde{\mathbf{v}} \sim p_A(\mathbf{v})$ , resulting in respective scenes $\mathbf{x} \sim p_A(\mathbf{x} \mid \mathbf{v}, \mathbf{c})$ and $\tilde{\mathbf{x}} \sim p_A(\mathbf{x} \mid \tilde{\mathbf{v}}, \mathbf{c})$ , for any homeomorphic transformation $h_x \in \mathcal{H}_x$ such that $h_x(\mathbf{x}) = \tilde{\mathbf{x}}$ , their exists another homeomorphic transformation $h_v \in \mathcal{H}_v$ such that $\mathcal{H}_v \subseteq \mathcal{H}_x \subseteq \mathbb{R}^{\dim(\mathbf{x})}$ and $\mathbf{v} = h_v^{-1}\left(f_d^{-1}(h_x(\mathbf{x}); \mathbf{c})\right)$ .
+
+Remark 5.6. Note that the theorem only says that the transformation function transforming the view representations $\mathbf{v}$ as an effect of the homeomorphic transformation of $\mathbf{x}$ lies in the same subspace of input transformations.
+
+In the scenario when the cameras are positioned such that they have overlapping fields of view, and their relative pose (rotation and translation) must avoid degeneracies like aligning on the same plane or mapping points to infinity. This results in the transformation between views being smooth, invertible, and consistent. If the scene is planar or depth variations are minimal, the homography can capture the transformation accurately without the need for inverse rendering. Notably, the cameras should have non-zero rotation and translation to avoid collapsing the scene, and their intrinsic parameters must be known or identical to prevent distortions. When the scenario satisfies all the above properties, the 2D homography transformation $\mathbf{H}$ between two camera views can be learned as a homeomorphic transformation (Hartley & Zisserman, 2003).
+
+*Proof Sketch.* We prove the following result by following the steps in Thm. 5.4, over a view distribution $p(\mathbf{v})$ but for a fixed content vector $\mathbf{c}$ .
+
+Given the work's theoretical focus, experimentally, we aim to provide strong empirical evidence of our identifiability, invariance, and equivariance claims in a multiview setting. We also extend our experiments to standard imaging benchmarks, including CLEVR-MV, CLEVR-AUG, GQN (Li et al., 2020); we additionally demonstrate the framework's scalability to highly diverse setting with GSO (Downs et al., 2022) and proposed datasets MV-MOVIC, MV-MOVID which are multiview versions of MoViC dataset with fixed and varying scene-specific cameras (Greff et al., 2022).
+
+**Experimental setup.** To verify our claims on (i) identifiability claim, we train our model on a given view subset $A \subseteq [V]$ and compare view averaged slot mean correlation coefficient (SMCC) measure as defined (Kori et al., 2024) $(\mathrm{SMCC}(\mathbf{s}, \tilde{\mathbf{s}}) \coloneqq \frac{1}{Kd} \sum_{i=0}^{d} \sum_{j=0}^{d} \rho(\mathbf{s}_{ij}, A\tilde{\mathbf{s}}_{\tau(i)j}) \text{ for some }$ permutation map $\tau$ and affine transformation $\boldsymbol{A}$ ), (ii) invariance claim, we train multiple models on different subsets of viewpoints $A, B \subseteq [V]$ and compare the aggregate content representations across models, quantifying the similarities with SMCC, we consider this measure to be invariant SMCC (INV-SMCC), and finally, (iii) for subspace equivariance, we consider a trained model with a view subset $A \subseteq [V]$ and compute MCC of view information v by applying random homeomorphic transformations on samples $\mathbf{x}^A \sim \mathcal{X}^A$ (which can also be done by considering samples $\mathbf{x}^B \sim \mathcal{X}^B$ , where cameras relative position satisfy the required constraints 5.5, and analyse $p(\mathbf{v}^A)$ and $p(\mathbf{v}^B)$ ).
+
+**Models & baselines.** We consider two ablations with two types of decoders: (i) additive with MLPs and spatial broadcasting CNNs and (ii) non-additive decoders, which include transformer models. In all cases, we use LeakyReLU activations to satisfy the weak injectivity conditions (Assumption F.4). In terms of object-centric learning baselines, we compare with standard additive autoencoder setups following (Brady et al., 2023), slot-attention (SA) (Locatello et al., 2020b), probabilistic slot-attention (PSA) (Kori et al., 2024), MulMON (Li et al., 2020), and OCLOC (Yuan et al., 2024).
+
+**Architectures:** As detailed in the paper we use two different classes of decoder architectures: (i) additive and (ii) non-additive; within the additive architecture we use both spatial broadcasting and MLP decoders, for the non-additive architecture we use transformer decoders. Concretely, we follow SA (Locatello et al., 2020b) for spatial broadcasting decoders and DINOSAUR (Seitzer et al., 2022) for both MLP and transformer decoder. In detail we use:
+
+1. **Spatial broadcasting decoders** - Input/Output: The generated slots are $\mathbf{s} \in \mathbb{R}^{K \times d}$ , each slot representation is broadcasted onto a 2D grid of dimension $8 \times 8 \times d$ and augmented with position embeddings. Similar to slot attention, each such grid is decoded using a
+
+shared CNN to produce an output of size $W \times H \times 4$ , where W and H are width and height of the image, respectively. The output channels encode RGB color channels and an (unnormalized) alpha mask. Further, we normalize the alpha masks with Softmax and perform convex combinations to obtain reconstruction. Shared CNN architecture: $3 \times [\text{Conv}(kernel = 5 \times 5, stride = 2), \text{LeakyReLU}(0.02)] + \text{Conv}(kernel = 3 \times 3, stride = 1), \text{LeakyReLU}(0.02)$
+
+- 2. **MLP decoders** Input/Output: similar to the spatial broadcasting decoder, here, each slot representation is broadcasted onto N tokens (resulting in $N \times d$ ) and augmented with position embeddings. Then the individual slot representation is transformed with a shared MLP decoder to generate a representation corresponding to feature dimension along with additional alpha mask, which is further normalised with Softmax and used in creating convex combinations to obtain reconstruction. Shared MLP architecture: [Linear(d, d, bias = False), LayerNorm(d)] + 3 × [Linear(d, d, d, d, d, d, d, d,
+- 3. **Transformer decoders**: Input/Output- transformer consists of linear transformers encoder output $(N \times d)$ and extracted slots $(K \times d)$ as input, while returning the slot conditioned feature as output with a dimension of $(N \times d)$ . Transformer architecture: is made up of 4 transformer blocks, where each transformer block consists of a self-attention on input tokens, cross-attention with the set of slots, and residual two-layer MLP with hidden size $4 \times d$ . Before the Transformer blocks, both the initial input and the slots are linearly transformed to d, followed by a layer norm.
+
+To definitively show the validity of our claims about identifiability (Thm 5.3, Thm 5.4, and Thm 5.5), we created a synthetic unconfounded scenario for modelling. This provides us with two data modalities, Fig, 7 (i) projected point cloud data, and (ii) corresponding imagery data, we detail point cloud illustrations in appendix G.1. Additionally, this dataset also provides us with the ground truth object and viewpoint features for evaluation. To visualise the aggregate mixture, following Lemma F.2, we use the projected GMM to interpret the distribution of random variables in $\mathbb{R}^d$ .
+
+The data-generating process is thoroughly explained in the App. D.1. In Fig. 5, we display the distributions of marginalized aggregate content distribution $q(\mathbf{c})$ , comparing individual features and a mean feature across different runs that are either scaled, shifted, or split (increase in number of modes), which is reflective of affine transformation of features across runs. To quantitatively measure the same, we
+
+
+
+Figure 5. Identifiability of q(c). The top row indicates individual feature distribution across five different runs. The bottom row reflects the feature feature distribution, which we use as a proxy for multi-dimensional features given Lemma F.2. As observed, mean feature distribution across runs is either scaled, shifted, or split (increase in number of modes); this provides strong evidence of recovery of the latent space up to affine transformations, empirically verifying our claims in Thm. 5.3.
+
+Table 1. Comparing identifiability of q(s), q(c), and p(v) scores wrt existing OCL methods.
+
+| METHOD | CLEVR-MV | | | GQN | | | GSO | | |
+|--------|------------|------------|------------|------------|------------|------------|-------------|-------------|-------------|
+| | SMCC ↑ | INV-SMCC ↑ | MCC ↑ | SMCC ↑ | INV-SMCC ↑ | MCC ↑ | SMCC ↑ | INV-SMCC ↑ | MCC ↑ |
+| AE | 0.32 ± .02 | - | - | 0.29 ± .02 | - | - | 0.24 ± 0.08 | - | - |
+| SA | 0.47 ± .03 | - | - | 0.38 ± .02 | - | - | 0.28 ± 0.06 | - | - |
+| PSA | 0.49 ± .02 | - | - | 0.38 ± .02 | - | - | 0.30 ± 0.04 | - | - |
+| MulMON | 0.61 ± .03 | 0.62 ± .02 | - | 0.59 ± .06 | 0.61 ± .02 | - | 0.56 ± 0.04 | 0.48 ± 0.06 | - |
+| OCLOC | 0.63 ± .02 | 0.64 ± .01 | 0.48 ± .04 | 0.60 ± .03 | 0.60 ± .01 | 0.42 ± .08 | 0.58 ± 0.04 | 0.54 ± 0.03 | 0.46 ± 0.04 |
+| VISA | 0.67 ± .01 | 0.66 ± .01 | 0.60 ± .04 | 0.59 ± .01 | 0.63 ± .01 | 0.52 ± .03 | 0.60 ± .03 | 0.61 ± .02 | 0.58 ± .03 |
+
+Table 2. Identifiability and generalisability analysis on MV-MOVIC dataset.
+
+| METHOD | | | IN-DOMAIN RESULTS | | OUT-OF-DOMAIN RESULTS | | | |
+|-----------------------------------|------------------------------|------------------------------|-------------------|--------------|------------------------------|------------------------------|--------------|--------------|
+| | mBO ↑ | SMCC ↑ | INV-SMCC ↑ | MCC ↑ | mBO ↑ | SMCC ↑ | INV-SMCC ↑ | MCC ↑ |
+| SA-MLP PSA-MLP | 0.28 ± 0.091 0.30 ± 0.022 | 0.36 ± 0.004 0.38 ± 0.002 | - - | - - | 0.26 ± 0.08 0.30 ± 0.03 | 0.38 ± 0.006 0.40 ± 0.005 | - - | - - |
+| VISA-MLP | 0.28 ± 0.021 | 0.52 ± 0.021 | 0.61 ± 0.023 | 0.54 ± 0.026 | 0.27 ± 0.02 | 0.51 ± 0.029 | 0.58 ± 0.031 | 0.52 ± 0.021 |
+| SA-TRANSFORMER PSA-TRANSFORMER | 0.34 ± 0.014 0.37 ± 0.021 | 0.36 ± 0.016 0.38 ± 0.007 | - - | - - | 0.33 ± 0.041 0.37 ± 0.033 | 0.36 ± 0.043 0.39 ± 0.016 | - - | - - |
+| VISA-TRANSFORMER | 0.38 ± 0.008 | 0.44 ± 0.003 | 0.46 ± 0.001 | 0.53 ± 0.011 | 0.36 ± 0.017 | 0.46 ± 0.033 | 0.46 ± 0.018 | 0.55 ± 0.082 |
+
+
+
+Figure 6. Viewpoint invariance for q(c). The top and bottom row indicates individual feature levels and mean feature distributions, respectively. Each columns reflect marginalised aggregate content distribution q(c) when trained with different view pairs {(blue, red), (green, blue), and (green, red)}, respectively. As the resulting distributions with different datasets only vary by an affine transformation, providing strong evidence for Thm. 5.4.
+
+computed SMCC and observed it to be 0.72 ± 0.04, empirically verifying our Thm. 5.3. Furthermore, to illustrate
+
+the invariance of distribution q(c) across viewpoints (Thm. 5.4), we consider three different viewpoints. We use all possible pairs to learn q(c) distributions as illustrated in Fig. 6, where the distributions are described w.r.t viewpoints described by {g, r}, {r, b}, and {g, b}, respectively. These distributions were also found to have similar properties as before, with an observed SMCC of 0.71 ± 0.11, further confirming the claims in Thm. 5.4. Additionally, Fig. 2 demonstrates the improvement in identifiability as the number of viewpoints increases.
+
+CASE STUDY 2: IMAGING APPLICATIONS. We first evaluate the framework on standard benchmarks, specifically focusing on CLEVR-MV, CLEVR-AUG, GQN, and GSO with simple objects. Given the *true generative factors* are unobserved, we derive our quantitative assessments from multiple runs. The results are shown in Table 1, confirming the validity of our theory on imaging datasets. Regarding the baseline comparisons that utilize a single viewpoint, the INV-SMCC mirrors the SMCC due to its inherent design (*i.e.,* aggregation of a set with a single element is the same element). Moreover, in the case of AE, SA, PSA, and
+
+MULMON, the models do not estimate view information but either treat them independently or use the observed view conditioning, rendering the MCC metric inapplicable. Fig. 12 showcases how the number of viewpoints impacts the identifiability of the s, v, and c variables; the involved experiments reflect the increase in performance with an increase in the number of views, across all benchmark datasets.
+
+Additionally, we demonstrate our methodology on proposed complex datasets, MV-MOVIC and MV-MOVID, the latter dataset enables us to examine the model performs when the assumption 5.1 is not satisfied. To evaluate model behaviour in an environment with consistent objects but with different viewpoints, we conducted in-domain and out-of-domain (OOD) evaluations. For in-domain analysis, the model is trained and assessed on the same viewpoint group A = [1, 2, 3]. Conversely, for OOD evaluation, we consider the previously trained model but test it against a new set of viewpoints B = [3, 4, 5]. The findings presented in Table 2 regarding the MV-MOVIC dataset reveal that the SMCC, INV-SMCC, and MCC metrics show similar performance across both domains. This indicates that the distributional characteristics remain unchanged when both the training and testing environments contain the same objects. The MV-MOVID dataset analysis can be found in App. G.
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+# Introduction
+
+Artificial Intelligence (AI) has undergone significant development, marked by a \"Cold War\" between symbolic and neural approaches. The symbolic approach dominated the early stages of AI research, but in recent years, neural models have gained prominence. Both approaches face distinct challenges, and this has paved the way for the emergence of the third wave of AI, known as Neurosymbolic AI [@garcez2023neurosymbolic]. Neurosymbolic AI combines the learning capabilities of neural models with the reasoning abilities of symbolic models. Simultaneously, the aviation and transportation industries are experiencing a transformative shift with the advent of Advanced Air Mobility (AAM). AAM envisions a future where air transportation is seamlessly integrated into everyday life, enabling efficient, safe, and sustainable movement of people and goods. This vision is being realized through technological advancements in electric propulsion, automation, and materials science, fostering the development of innovative aircraft such as electric vertical take-off and landing (eVTOL) vehicles [@johnson2022nasa].
+
+AAM faces several obstacles like regulatory complexity, the need for safe and explainable automated decision-making, and the integration of heterogeneous data from demand forecasting, infrastructure constraints, weather, and traffic conditions[@cohen2024advanced]. Neurosymbolic AI offers a promising solution by combining the pattern-recognition power of neural networks with the rule-based logic of symbolic systems, allowing for a more interpretable, high-level reasoning framework that can encode safety protocols, regulatory constraints, and domain-specific knowledge. As a result, decisions made by Neurosymbolic AI are not only data-informed but also grounded in explicit safety requirements and operational guidelines critical for fostering growth in AAM by improving trust, compliance, and the overall reliability of autonomous aerial operations.
+
+Neurosymbolic AI offers significant potential for AAM by combining neural learning from large, heterogeneous datasets (e.g., sensor streams, flight trajectories, air traffic patterns) with symbolic reasoning that enforces regulatory and safety constraints. This dual approach supports both Urban Air Mobility (UAM) and Regional Air Mobility (RAM) by ensuring models not only adapt to complex, real-time data but also adhere to airspace rules, vehicle performance limits, and scheduling constraints. In particular, Neurosymbolic Reinforcement Learning [@acharya2023neurosymbolic] can optimize flight paths, manage dynamic air traffic, and coordinate ground infrastructure in real time, balancing efficiency with safety and noise reduction. Embedding domain knowledge into the symbolic layer alongside adaptive neural policies allows continuous strategy refinement in AAM systems. However, developing robust Neurosymbolic architectures for challenges like multi-vehicle coordination, real-time constraint satisfaction, and large-scale deployment remains an open research area. This article categorizes key research domains essential for advancing AAM from engineering design and electrification to demand modeling, autonomy, cybersecurity, and public acceptance and outlines how Neurosymbolic AI can be applied across these areas, along with potential case studies and associated risks.
+
+While surveying existing literature, we observed no comprehensive review on the integration of Neurosymbolic AI within AAM. Prior studies have focused on isolated topics, demand modeling [@LONG2023102436], aircraft design [@KIESEWETTER2023100949], electrification [@dorn2020power], and safety [@yoo2022risk], but none have explored how Neurosymbolic AI can accelerate and enhance research across all AAM domains. This survey paper is the first to provide a detailed analysis of ongoing research efforts in AAM and to propose ways Neurosymbolic AI can drive advancements in this field.
+
+Neurosymbolic AI represents a hybrid approach that integrates neural networks' learning capabilities with symbolic AI's structured reasoning, overcoming the limitations of both paradigms. Symbolic AI, dominant from the 1950s to the 1980s, offers explainable reasoning and structured knowledge representation, but it requires extensive manual rule definitions [@mao2019neuro]. In contrast, connectionist AI, or deep learning, enables data-driven pattern recognition but suffers from black-box decision-making and interpretability challenges [@acharya2023neurosymbolic]. Neurosymbolic AI leverages the strengths of both by enabling efficient knowledge extraction and conceptual reasoning, requiring less training data while maintaining transparency and adaptability in uncertain environments [@garcez2023neurosymbolic]. Due to these advantages, Neurosymbolic AI is often regarded as the third wave of AI, capable of achieving error correction, enhanced explainability, and logical inference beyond deep learning alone. [1](#nesy){reference-type="ref+label" reference="nesy"} depicts the general interaction between the symbolic and connectionist frameworks in Neurosymbolic AI.
+
+{#nesy width="\\linewidth"}
+
+Several taxonomies have been proposed to classify Neurosymbolic AI systems. Henry Kautz proposed a classification system based on six distinctive categories [@kautz2022third]. More recently, a survey categorized Neurosymbolic AI based on efficiency, generalization, and interpretability, leading to three primary approaches: learning for reasoning, reasoning for learning, and learning-reasoning [@yu2023survey]. Learning for reasoning uses neural networks to enhance symbolic reasoning, reasoning for learning incorporates symbolic logic to refine neural learning processes, and learning-reasoning represents a fully integrated system where both paradigms influence each other dynamically. These frameworks provide a comprehensive approach to structuring Neurosymbolic AI for robust, explainable decision-making in various domains.
+
+Neurosymbolic AI has broad applications, particularly in intelligent transportation systems (ITS) and AAM. In ITS, Neurosymbolic AI enables real-time sensor integration with pre-defined traffic rules, ensuring adaptive yet regulatory-compliant AI-driven transportation solutions [@garcez2023neurosymbolic]. This improves traffic condition prediction, autonomous vehicle safety, and public transit optimization. Similarly, in AAM and UAM, Neurosymbolic AI enhances decision-making, safety, and airspace management. It aids Unmanned Aircraft Systems (UAS) in navigating complex environments by incorporating expert-defined flight safety rules and predictive modeling [@gilpin2021neuro]. By fusing symbolic reasoning with deep learning, Neurosymbolic AI improves reliability, demand forecasting, and autonomous decision-making in emerging aviation technologies, ensuring intelligent, safe, and interpretable air mobility operations [@kohaut2024probabilistic].
+
+AAM represents a paradigm shift in transportation, promising to revolutionize the movement of people and goods, particularly in urban and regional environments. This transformative vision encompasses a diverse array of applications, extending beyond the initial concept of air taxis to include UAM, cargo delivery, emergency medical services, and various other aerial missions [@goyal2022advanced]. [2](#fig:AAMDevelopment){reference-type="ref+label" reference="fig:AAMDevelopment"} provides the overview of technologies in advancement of AAM along with the applications areas.
+
+eVTOL aircraft are at the heart of the AAM revolution. Their electric propulsion systems offer several compelling advantages over traditional helicopters and airplanes. These include reduced noise pollution, significantly lower emissions contributing to a more sustainable transportation system, and the potential for autonomous operation [@LONG2023102436]. The reduced noise footprint is particularly important for urban environments, where noise pollution is a major concern [@rizzi2022urban]. However, substantial challenges remain in developing batteries with sufficient energy density and range to support commercially viable flight times and distances. Ensuring the safety and reliability of battery technology, including addressing potential failure modes and mitigating risks, is also crucial. Extensive research is underway to address these technological hurdles, focusing on improvements in battery technology, electric motor design, and overall aircraft design optimization [@barrera2022next].
+
+
+
+AAM Overview
+
+
+The safe and reliable integration of numerous autonomous aircraft into existing airspace demands the development of sophisticated Unmanned Traffic Management (UTM) systems [@namuduri2023digital]. These systems are crucial for managing air traffic flow, preventing mid-air collisions, and ensuring compliance with safety regulations. Research is actively exploring various approaches to UTM, including centralized and decentralized architectures. Centralized systems offer a more controlled approach to managing air traffic, while decentralized systems offer greater resilience and scalability. The choice between these architectures involves trade-offs between control and resilience, and the optimal approach may vary depending on the specific operational context [@murcca2024characterizing]. The integration of machine learning techniques is being explored to improve the efficiency and adaptability of UTM systems [@deniz2024reinforcement]. Furthermore, the development of advanced sensor technologies, such as lidar and radar, is crucial for enhancing the situational awareness of autonomous aircraft [@karampinis2024ensuring].
+
+Seamless data exchange between aircraft, ground control, and other stakeholders is essential for real-time monitoring, traffic management, and overall system safety [@do2024cellular]. The integration of cellular networks and other communication technologies plays a pivotal role in achieving this. The evolution of cellular technology, with 5G and the anticipated arrival of 6G networks, is expected to significantly enhance the capabilities of AAM communication systems. The use of advanced beam forming techniques can improve the efficiency and reliability of cellular connectivity in AAM scenarios. Researchers are also exploring the use of other communication technologies, such as satellite communication, to augment cellular networks and provide broader coverage [@do2024cellular]. Vehicle-to-vehicle (V2V) communication is essential for enabling safe and efficient coordination between aircraft [@gur2024v2v].
+
+UAM aims to alleviate traffic congestion, reduce commute times, and improve accessibility, particularly for areas with limited ground transportation infrastructure [@goyal2021advanced]. However, challenges remain in integrating UAM into existing urban environments, managing air traffic density, and addressing public concerns about noise and safety. UAM systems require the development of sophisticated airspace management strategies to handle the high density of aircraft operations. This includes defining flight corridors, establishing separation standards, and managing potential conflicts with other air traffic [@murcca2024characterizing]. The development of robust communication and navigation systems is also crucial for ensuring the safety and efficiency of UAM operations [@do2024cellular]. UAM operations need to be integrated with ground transportation systems to provide seamless multimodal travel options. This involves the development of vertiports and other infrastructure to facilitate the transfer between air and ground transportation. The integration of UAM into existing urban environments requires careful consideration of land use planning, community engagement, and public acceptance.
+
+AAM offers significant potential for improving cargo delivery and logistics, particularly in remote or underserved areas. Drones and other AAM vehicles can transport goods more efficiently and cost-effectively than traditional ground transportation, especially in areas with challenging terrain or limited road infrastructure. Applications include medical supplies delivery, package delivery, and the transportation of time-sensitive goods [@dulia2021benefits]. The integration of AAM into existing logistics networks requires coordination with ground transportation and warehousing systems [@raghunatha2023addressing]. The use of autonomous drones for cargo delivery can reduce labor costs and improve efficiency. However, ensuring the safety and security of autonomous drone deliveries is a critical concern [@gur2024v2v].
+
+eVTOL aircraft and drones can provide rapid transport of patients to hospitals, reducing response times and potentially improving patient outcomes. The use of AAM in EMS also has the potential to improve access to healthcare in underserved communities [@bridgelall2024transforming]. AAM based EMS systems require the integration of AAM vehicles with existing EMS infrastructure and protocols. The economic feasibility of AAM based EMS depends on factors such as operating costs, patient transport times, and the potential for improved patient outcomes. The strategic placement of vertiports near hospitals and other healthcare facilities can optimize response times and improve access to care. The development of efficient routing algorithms and real-time traffic management systems is crucial for optimizing AAM-based EMS operations [@bridgelall2024transforming].
+
+Neurosymbolic AI has been gaining popularity worldwide due to its efficient learning and reasoning capability in almost every domain. In context of AAM, main applications of Neurosymbolic AI is shown in the [3](#fig:applications){reference-type="ref+label" reference="fig:applications"}.
+
+
+
+Applications of Neurosymbolic AI in AAM
+
+
+Electrifying AAM is pivotal for achieving sustainable aviation by reducing emissions, lowering operational costs, and enabling quieter operations. However, the success of eVTOL aircraft is constrained by significant technological and operational challenges, notably the limitations of current lithium-ion battery (LIB) technology. Despite LIBs' high energy density (300--400 Wh/kg for short-range applications and over 800 Wh/kg for narrow-body aircraft), issues such as weight constraints, energy inefficiency, thermal instability, and inadequate charging infrastructure persist [@barrera2022next]. These challenges are exacerbated during high-demand phases like takeoff and landing, necessitating advanced energy management strategies. To address these issues, innovations such as wide bandgap semiconductors, improved thermal management systems, and advanced control strategies are being pursued to enhance the reliability and efficiency of electric propulsion systems [@dorn2020power]. Furthermore, Neurosymbolic AI is emerging as a promising tool for optimizing energy usage, extending flight range, and managing battery lifecycles through techniques like predictive energy optimization, smart grid integration, and fault detection.
+
+The design of eVTOL aircraft for air taxis and metro systems hinges on advancing battery storage, propulsion efficiency, and noise reduction to meet both safety and urban sustainability criteria. While manufacturers like Airbus are developing specialized eVTOL platforms, challenges persist in certification, infrastructure integration, and public acceptance [@silva2018vtol]. Regulatory frameworks need to evolve alongside these technologies, as highlighted by NASA/Deloitte Concept of Operations (CONOPS) [@hill2020uam], and user-centric design elements, such as cabin noise and ride comfort, are critical for adoption. Design trade-offs between payload capacity, flight range, and vehicle weight continue to constrain the scalability of eVTOL systems [@KIESEWETTER2023100949]. Moreover, distinct requirements for intra versus inter-city passenger transport, along with the differing design challenges of UAM aircraft and last-mile delivery drones, call for a multifaceted engineering strategy that incorporates aerodynamic efficiency, AI-driven control systems, and optimized energy management. Despite promising advances in control mechanisms, propulsion, and VTOL transition efficiency, bridging the gap between technological potential and real-world deployment remains a significant challenge, underscoring the need for an interdisciplinary approach integrating engineering, policy, and urban planning.
+
+Training and simulation are essential for advancing AAM by creating adaptive environments for skill development and operational planning. Neurosymbolic AI, which synergizes the robust pattern recognition capabilities of deep learning with the interpretability and reasoning strengths of symbolic AI, can significantly enhance simulation platforms[@siyaev2023interaction]. By integrating Neurosymbolic AI, simulation systems can not only process vast amounts of sensor and operational data through deep learning but also incorporate domain-specific rules and logical reasoning to better replicate the intricate dynamics of AAM environments. This approach enables the development of training scenarios that are both realistic and adaptable, addressing complex operational variables such as weather variations, urban infrastructure constraints, and emergency protocols. Moreover, Neurosymbolic AI facilitates the creation of digital twins that can reason about potential system failures, optimize route planning, and simulate multi-agent interactions with high fidelity[@siyaev2023interaction].
+
+Predictive maintenance is crucial for enhancing safety and operational efficiency in AAM. By leveraging vast arrays of sensor data and advanced analytics, predictive maintenance systems can identify early indicators of potential component degradation or system malfunctions before they escalate into critical failures[@nguyen2022artificial]. Neurosymbolic AI is used in fault diagnosis by integrating a case-based reasoning (CBR) method with a Bayesian network (BN) approach, where fault cases are initially formalized for similarity-based diagnosis and later transformed into a probabilistic model for dynamic multi-fault detection under uncertainty [@yang2018optimized]. Neurosymbolic reasoning [@siyaev2023interaction] enhances digital twins by enabling natural language interaction, allowing users to manipulate 3D components, read maintenance manuals, and perform installation and removal procedures autonomously. The result is not only a reduction in unexpected downtime, but also a significant improvement in the lifecycle management of critical systems, ultimately leading to safer and more efficient air mobility operations.
+
+Safety is a critical aspect of AAM due to the inherent risks and complexities associated with integrating new aerial systems into urban and regional airspaces [@yoo2022risk]. According to NASA's In-Time System-wide Safety Assurance (ISSA) framework, hazards in AAM can arise from multiple sources, including vehicle malfunctions, environmental factors, operational contexts, and broader aviation system challenges [@ellis2020time]. The National Transportation Safety Board (NTSB) emphasizes that AAM safety standards must exceed those of general aviation, given the high volume of short-distance flights expected in AAM operations [@national2018time]. To ensure safety in AAM, one approach involves using symbolic logic constraints driven by common rules, often derived from Federal Aviation Administration (FAA) regulations, to filter or \"shield\" the outputs of neural networks, preventing unsafe actions during the training phase [@jansen2020safe]. To this end, a Neurosymbolic Deep Reinforcement Learning approach has been proposed to enhance safety by embedding symbolic logic constraints directly into the learning process [@sharifi2023towards]. Another strategy involves guiding neural networks using logical safety rules, providing feedback to the AI agent regarding the safety of its actions [@kimura2021reinforcement].
+
+AAM envisions high-density, low-altitude operations in urban and regional environments, requiring aerial vehicles to navigate dynamic obstacles, weather conditions, and air traffic without direct human intervention. Autonomous systems reduce pilot workload, lower operational costs, and enhance safety by making real-time decisions based on sensor data and predefined regulations. Autonomy ensures seamless integration with existing Air Traffic Management (ATM) systems, facilitating coordination between manned and unmanned aerial vehicles (UAVs). By embedding symbolic representations of air traffic rules, safety protocols, and operational guidelines, Neurosymbolic systems can ensure compliance with aviation standards, avoiding collisions and potential conflicts, while dynamically adapting to changing conditions. Moreover, Neurosymbolic AI tackles the overlying scalability issue in traditional connectionist frameworks by integrating symbolic knowledge (e.g., air traffic rules, safety protocols) with neural network-based perception and control systems, allowing autonomous AAM vehicles to scale across diverse environments with minimal retraining [@wang2024towards].
+
+AAM cybersecurity challenges stem from their reliance on interconnected systems, autonomous decision-making, and real-time data exchange. Critical threats include GPS spoofing, wireless attacks (e.g., jamming and man-in-the-middle), AI adversarial manipulations, and cyber-physical intrusions, all of which can compromise navigational accuracy and flight control [@petit2014potential]. Unlike conventional deep learning models, which are prone to adversarial vulnerabilities, Neurosymbolic AI enhances security by integrating logical rules, knowledge graphs, and domain constraints. For instance, Logic Tensor Networks can merge formal security policies with real-time anomaly detection to filter malicious network activity [@grov2024use], while Neurosymbolic methods also enable cross-validation of multiple navigational inputs to detect and correct GPS spoofing. Neurosymbolic AI driven Security Operations Centers have proven effective in cyber threat intelligence and adaptive incident response, offering a robust defense framework for AAM's digital infrastructure.
+
+AAM demand modeling is challenged by complex, multifaceted data and dynamic operational environments that traditional models often fail to capture [@LONG2023102436]. Neurosymbolic AI, which fuses deep neural networks' pattern recognition with the structured reasoning of symbolic systems, has emerged as a promising yet still exploratory approach in this domain [@garcez2023neurosymbolic]. Key hurdles include aligning unstructured sensor and user behavior data with the symbolic representations of regulatory and operational constraints, thereby affecting model interpretability, scalability, and robustness. Recent advancements, such as the integration of decision trees with neural networks [@acharya2025neurosymbolic] and the development of Probabilistic Mission Design architectures [@kohaut2024probabilistic], have demonstrated improved forecasting accuracy and novel methods for embedding legal frameworks into AI models.
+
+The FAA Roadmap for AI Safety Assurance[^1] establishes a structured, phased approach for integrating artificial intelligence into aviation, a domain traditionally governed by deterministic safety protocols. The roadmap advocates for an incremental, risk-based strategy, initially focusing on low-criticality systems to gather operational data and refine safety assurance processes before transitioning to high-stakes, safety-critical applications. This iterative engineering approach ensures that early-stage insights inform the deployment of more complex AI-driven systems. A key aspect of the roadmap is its distinction between learned AI (static, pre-trained models) and learning AI (adaptive, real-time evolving systems). While learning AI enhances adaptability and responsiveness, it poses significant certification challenges due to its inherent unpredictability, limited explainability, and potential for bias issues that conventional safety methodologies struggle to address.
+
+The roadmap underscores the importance of regulatory alignment with existing aviation safety frameworks, advocating for industry collaboration among regulatory bodies, government agencies, and stakeholders to establish standardized AI assurance protocols. However, it highlights critical gaps, including the lack of robust certification methods for evolving AI, inadequate bias mitigation strategies, and the absence of comprehensive regulatory guidelines for autonomous AI-driven aviation systems. Ethical considerations, essential for public trust and system accountability, receive minimal focus, indicating an area requiring further exploration. Addressing these challenges is essential for ensuring safe, reliable AI integration into aviation while maintaining compliance with rigorous industry standards. By integrating Neurosymbolic AI, the FAA can establish a robust, certifiable AI framework that enhances aviation safety while accelerating the adoption of AI in autonomous flight operations.
+
+EASA AI Roadmap 1.0[^2] is the initial roadmap established a foundational strategy for integrating AI---primarily machine learning and deep learning---into aviation, addressing its transformative potential across aircraft design, operations, maintenance, air traffic management, drone applications, and safety risk management. It underscored the critical need for robust trustworthiness frameworks through enhanced explainability, human-AI collaboration, and phased certification processes to ensure safety and reliability. EASA AI Roadmap 2.0[^3], expands the scope by incorporating hybrid AI approaches such as Neurosymbolic methods, while further emphasizing cybersecurity and AI safety assurance within a human-centric framework. This version reaffirms AI's role as an augmentation tool, integrating advanced digital twin technologies and predictive maintenance to enhance operational efficiency and safety. Aligned with the regulatory requirements of the EU AI Act, it introduces risk-based categorization of AI systems, mandating transparency, explainability, and human oversight.
+
+Neurosymbolic AI has the potential to enhance the efficiency, safety, and scalability of AAM, but it also presents several challenges and risks as depicted in the [4](#fig:risks){reference-type="ref+label" reference="fig:risks"}.
+
+
+
+Risks and Challenges
+
+
+Data integration in aviation face challenges due to heterogeneous sensor sources. Multi-sensor fusion can be homogeneous or heterogeneous, with the latter requiring precise synchronization. AI enhanced integration with air traffic management demands legacy system re-engineering while ensuring safety compliance[@su16198336]. Interoperability is critical, necessitating standardized data formats, exchange protocols, and interface standards. The FAA's Next Gen program focuses on digital system interoperability to support AI-driven airspace operations [^4]. Advanced fusion methods leverage variance and covariance estimations for optimal integration, while AI driven standardization enhances cross-platform data accessibility, as seen in the U.S. military's Combined Joint All Domain Command and Control (CJADC2) [^5] system.
+
+The integration of neural networks and symbolic reasoning, although potentially powerful, introduces new complexities in system design and validation[@jalaian_neurosymbolic_2023]. Ensuring the robustness of these hybrid systems against adversarial attacks targeting both the neural and symbolic components requires extensive testing under diverse, real-world conditions [@hagos2024neuro]. Moreover, the interpretability of Neurosymbolic models, crucial for safety-critical applications like AAM, remains an ongoing research challenge. The dynamic nature of cyber threats also necessitates continuous adaptation of these systems, raising questions about long-term reliability and maintenance [@hagos2024neuro]. The increased computational requirements of Neurosymbolic AI may pose challenges for real-time decision-making in resource-constrained AAM environments[@jalaian_neurosymbolic_2023].
+
+As AAM systems continually evolve, the necessity for robust version control, formal verification, and comprehensive testing becomes paramount to maintain both operational integrity and certification. Moreover, the scalability of these frameworks is tested by the heterogeneous nature of AAM fleets, which encompass vehicles with diverse sensor modalities and performance profiles, potentially leading to compatibility issues and performance bottlenecks if not managed through modular and adaptive architectures [@kohaut2024probabilistic]. While the modularity of Neurosymbolic systems offers a pathway to seamless integration and system evolution, their practical implementation in AAM must address the inherent trade-offs between adaptability and the rigorous demands of safety and regulatory compliance.
+
+Accountability issues remain particularly complex in scenarios where autonomous decisions lead to system failures or incidents, determining responsibility among manufacturers, programmers, and operators is nontrivial, highlighting the urgent need for clear regulatory frameworks and transparent decision-making processes that can apportion accountability effectively [@novelli2024accountability]. The integration of neural and symbolic methods, while promising enhanced interpretability, does not inherently mitigate risks related to bias and fairness [@ferrer2021bias]. Inadequate or skewed training data may propagate discriminatory outcomes, thereby compromising equitable service delivery across diverse operational contexts. Addressing these concerns requires embedding explicit fairness constraints and ethical auditing protocols within the Neurosymbolic framework to ensure decisions are both transparent and impartial.
+
+Transparency and explainability foster trust and enable scrutiny of automated decision-making processes. By integrating symbolic reasoning with neural networks, these systems can provide more interpretable decision pathways, allowing stakeholders to understand the rationale behind actions taken. Furthermore, implementing robust auditability mechanisms through comprehensive logging and traceability is essential for post-incident reviews, system improvement, and regulatory compliance. These features are vital for maintaining public trust and developing a resilient framework that can adapt to the evolving demands of AAM environments. However, it is important to note that achieving a balance between transparency and system performance remains a challenge, as increasing explainability may impact the efficiency of complex models [@acharya2023neurosymbolic].
+
+Public trust and social acceptance are crucial for addressing concerns related to data privacy, ethics, and system accountability in the rapidly evolving field of AAM.Despite hybrid approaches like Probabilistic Mission Design (ProMis) [@kohaut2024probabilistic] aiming to enhance transparency by merging symbolic reasoning with neural networks, significant risks remain. Current systems still fall short of fully explaining complex decision-making processes, undermining accountability and eroding public trust. Moreover, without sustained, effective collaboration among regulators, manufacturers, and the public, the ethical and responsible deployment of AAM systems is jeopardized, potentially stalling their broader acceptance and market integration. The transparency is particularly important when dealing with sensitive operational data and ensuring compliance with data protection regulations[^6].
+
+The FAA has made limited progress in determining appropriate certification paths for AAM aircraft, partly due to the lack of established airworthiness standards and operational regulations for novel features [^7]. This regulatory uncertainty is compounded by the need for new approaches to verify and validate Neurosymbolic systems, which combine probabilistic neural components with deterministic symbolic rules. The development of robust testing protocols that can rigorously verify system behavior under rare or extreme conditions remains an unresolved challenge, requiring advanced simulation environments, stress-testing methodologies, and formal verification techniques.
+
+# Method
+
+The dual nature of Neurosymbolic systems, merging the adaptive, data-driven insights of neural networks with the clear, rule-based logic of symbolic reasoning, complicates the clear attribution of accountability in incident scenarios. This complexity necessitates the establishment of legal guidelines that delineate responsibility among manufacturers, programmers, and operators, ensuring that each stakeholder is adequately accountable for AI-driven decisions. Moreover, given the global nature of AAM operations, harmonizing international standards becomes imperative to ensure that AI systems adhere to uniform safety and legal norms across diverse regulatory landscapes [@bengio2025international]. Achieving this will require coordinated efforts between legal experts, regulatory bodies, and technical developers to create transnational frameworks that not only standardize certification protocols but also provide mechanisms for continuous oversight and post-incident analysis.
+
+The implementation of dynamic, adaptive policy frameworks is essential to ensure compliance with evolving data protection laws and international privacy standards. This approach requires continuous updates to data management practices, aligning with regulations like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) [@van2021data]. Transparency in data use is achieved through standardized protocols for data sharing among stakeholders, while sensitive information is protected using advanced encryption, anonymization, and access control measures. This focus on compliance and transparency not only builds trust among stakeholders but also creates a resilient ecosystem with meticulous documentation of data provenance and maintenance of audit trails for rigorous oversight [^8].
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+# Introduction
+
+Graph learning is a powerful tool to process graph-structured data. Most graph learning methods, especially graph neural networks (GNNs), are trained on large-scale centralized graph data. These methods have been extensively applied in various domains, including drug discovery [@{RBX20+}; @{YWZ24+}], social networks [@{YZW20+}; @{ZGP22+}], knowledge graphs [@CWZ23; @{BCX23+}], and recommender systems [@{YPZ20+}; @{MRX22+}]. However, in many real-world scenarios, graph data are geographically distributed and cannot be directly shared due to privacy concerns. For example, in bioinformatics, graph classification techniques are employed to learn protein representations and classify them as enzymes or non-enzymes. These private protein molecules typically reside with multiple individual owners (i.e., clients). Due to intellectual property concerns and the intrinsic commercial value of molecules, academic labs, national labs, and private organizations are often unable to share their molecular data, leading to the data isolation problem.
+
+
+
+Graph data heterogeneity. Intra-domain heterogeneity involves two cases: first, clients possess data drawn from the same dataset but with inconsistent statistical properties; second, clients’ data are sourced from different datasets within the same domain. Inter-domain heterogeneity implies that clients’ data are derived from entirely different domains.
+
+
+Federated graph learning (FGL), a distributed learning paradigm for collaboratively training GNN models, has emerged as a promising direction for exploiting GNNs on decentralized graph data [@{TLL23+}; @{HWY23+}]. A major challenge in FGL is the underlying discrepancy in graph data distribution across clients, commonly referred to as graph data heterogeneity. As illustrated in Fig. [1](#fig:fig1){reference-type="ref" reference="fig:fig1"}, the heterogeneity manifests both intra and inter domains. Intra-domain heterogeneity arises when clients hold graph data from the same dataset or domain (e.g., small molecules) but with distinct statistical properties. In contrast, inter-domain heterogeneity occurs when each client's graph data is drawn from different domains, leading to discrepancies in both statistical characteristics and graph structural information. Such heterogeneity can severely destabilize the training process and degrade performance. While traditional federated learning approaches can effectively alleviate statistical inconsistency in computer vision tasks [@{MMR17+}; @{KKM20+}; @{LSZ20+}; @{WLL20+}], they fall short when it comes to handling heterogeneous graph data.
+
+Prior studies have sought to address the graph data heterogeneity problem from multiple perspectives, including optimization-based methods [@{LSZ20+}; @{KKM20+}], personalized FGL [@{ZMC23+}; @{TLL23+}], and clustered FGL [@{CMG21+}; @{XMX21+}]. For example, FedEgo [@{ZMC23+}] and FedStar [@{TLL23+}] share graph structural information across clients and introduce personalized feature knowledge learning modules on each client. FedCG [@{CMG21+}] and GCFL+ [@{XMX21+}] group clients into non-overlapping clusters, within which clients share model parameters. However, personalized approaches introduce additional trainable parameters, while clustered approaches are highly sensitive to the quality of client clustering. Since GNNs are typically over-parameterized [@ZSC23], all of these methods incur substantial computational cost and communication overhead [@{LDC22+}], and they still suffer from the following issues:
+
+1. GNN models trained under the empirical risk minimization (ERM) based optimizers tend to overfit local graph data and fail to generalize to out-of-distribution data.
+
+2. Heterogeneous graph data can cause local GNN models to undergo dimensional collapse in the representation space (as illustrated in Fig. [\[fig:fig2a\]](#fig:fig2a){reference-type="ref" reference="fig:fig2a"} and Fig. [\[fig:fig2c\]](#fig:fig2c){reference-type="ref" reference="fig:fig2c"}).
+
+To address the first issue, we note that models trained with ERM-based optimizers may fall into the sharp valley of the loss surface [@{FKM21+}], which leads to poor generalization, even when the training and test data distributions are similar. Previous efforts [@{DPB17+}; @{PAS17+}; @{NDJ17+}] have explored the relationship between the flatness of local minima and the generalization ability of deep models to improve generalization and have applied it within federated learning [@CCC22], but their application to FGL has not been thoroughly investigated. In FGL, each local GNN model only has access to a subset of the global graph data and thus tends to overfit its local data to minimize the training loss, which harms its ability to generalize to the test data. This study investigates the higher-order information underlying GNN generalization, namely, the curvature of the loss surface, to enhance the generalization ability of as many local GNN models as possible. To this end, we equip each local GNN model with a sharpness-aware minimization optimizer that jointly optimizes for low training loss and a flatter loss landscape. The objective is to identify model parameters that reside in a neighborhood with uniformly low loss values, rather than at a single point that simply minimizes the loss, which naturally leads to a min--max optimization problem. We solve it via a two-step procedure: (1) applying gradient ascent to find a perturbation point that increases the loss within a fixed-radius neighborhood of the current parameters, and (2) updating the current parameters by gradient descent using the gradient evaluated at this perturbed point.
+
+To address the second issue, we observe a phenomenon of dimensional collapse in the representation space of local GNNs, particularly under heterogeneous graph data distributions (Fig. [2](#fig:fig2){reference-type="ref" reference="fig:fig2"}). Specifically, graph representations learned by a three-layer graph attention network (GAT) [@{VCC18+}] exhibit severe collapse, forcing them to reside in a low-dimensional space and impeding class discriminability. In the FGL framework, the periodic aggregation and subsequent broadcasting of GNN backbones exacerbate this degradation[^1]. To mitigate this effect, we propose to decorrelate local graph representations. Since the singular values of the representation covariance matrix offer a rigorous characterization of their high-dimensional distribution, we introduce a regularization term to the local optimization objective to suppress these correlations. Considering the high computational complexity of full singular value decomposition, we instead normalize the covariance matrix into a correlation matrix and minimize its norm. This approach effectively encourages the model to learn more diverse and discriminative representations.
+
+In this study, we propose SEAL, a **S**harpness-aware f**E**derated gr**A**ph **L**earning algorithm. It integrates a sharpness-aware minimization (SAM) optimizer with a covariance-based decorrelation regularizer into the local training objective. Extensive evaluations across diverse graph classification benchmarks spanning four different domains demonstrate that SEAL consistently outperforms state-of-the-art FGL baselines, delivers excellent generalization, and adapts effectively to heterogeneous graph data distributions.
+
+
+
+ and show the representations generated by a randomly sampled client w/o and w/ representation decorrelation (RepDec) under the non-IID setting (COLLAB dataset), respectively. and show the representations under the inter-domain setting (BioSnCV), respectively. The representations are all generated by a three-layer graph attention network and then normalized into a unit ball.
+
+
+# Method
+
+Let $G = (\mathcal{V}, \mathcal{E})$ be a graph composed of a set of nodes $\mathcal{V}$ and a set of edges $\mathcal{E}$ connecting these nodes. Each node $v \in \mathcal{V}$ is associated with a feature vector $x_v$. A GNN aims to learn representations that capture both the graph structure and node features, including node representation $h_v$ for each node $v \in \mathcal{V}$ and graph representation $h_G$ for the entire graph $G$. It typically consists of a message propagation and a neighbor aggregation module, where each node $v$ iteratively collects and aggregates representations from its neighbors with its own. Formally, for an $L$-layer GNN $f$, the update at layer $l$ is given by $$\begin{equation}
+ h_v^{l+1} = \sigma \left[W h_v^l+U\mathrm{Agg}\left( \left\{ h_u^l; u \in \mathcal{N}_v \right\} \right) \right] , \forall l \in \left[ L \right], \label{eq:eq1}
+\end{equation}$$ where $h_v^{l}$ is the representation of node $v$ at the $l$-th layer, $h_v^{0} = x_v \in \mathbb{R}^{d_{v}}$ is the input node feature, $\mathcal{N}_v$ denotes the set of neighbors of $v$, $\mathrm{Agg}(\cdot)$ is an aggregation function (e.g., mean), $W, U\in \mathbb{R}^{d_{v}\times d_{v}}$ are learnable parameters and $\sigma$ represents an activation function (e.g., ReLU). To obtain the graph representation $h_G$, a $\mathrm{Readout}$ function is applied: $$\begin{equation}
+ h_G = \mathrm{Readout} \left( \left\{ h_v^{L+1}; v \in \mathcal{V} \right\} \right), \label{eq:eq2}
+\end{equation}$$ where $\mathrm{Readout}(\cdot)$ aggregates all node representations. Common choices for $\mathrm{Readout}(\cdot)$ include sum pooling, mean pooling, and max pooling.
+
+In federated learning, $N$ clients are connected to a central server responsible for model aggregation. The $i$-th client ($i \in [N]$) owns a private dataset $\xi_i = (X_i, Y_i)$ consisting of $M_i$ data instances sampled from the distribution $\mathcal{D}_i$. Due to data heterogeneity, these distributions $\mathcal{D}_i$ differ across clients. Let $\mathcal{L}_i(w_i)$ denote the training loss on client $i$: $\mathcal{L}_i(w_i) := \sum_{\xi_{i, j}} \ell(w_i, \xi_{i, j})$, where $\ell(w_i, \xi_{i, j})$ is the loss value of the $j$-th data instance and $w_i$ represents the parameters of the local model $f_i$ on client $i$. Federated learning performs the following global optimization by solving the ERM problem on each client: $$\begin{equation}
+ \min_{\left(w_1, w_2, \cdots, w_N\right)} \frac{1}{N} \sum_{i \in [N]} \mathcal{L}_i (w_i). \label{eq:eq3}
+\end{equation}$$
+
+The goal of federated learning is to learn a globally shared model $f$ with parameter $w = w_1 = w_2 = \cdots = w_N$. The most well-known approach, FedAvg [@{MMR17+}], periodically aggregates local models on the server via $$\begin{equation}
+ \bar{w} \leftarrow \sum_{i=1}^N \frac{M_i}{M} w_i, \label{eq:eq4}
+\end{equation}$$ and then broadcasts the aggregated model $\bar{w}$ back to all clients, where $M = \sum_{i} M_i$ is the total number of data instances across all clients. Each client receives $\bar{w}$ from the server and performs local stochastic gradient descent (SGD) in parallel.
+
+In this study, we focus on inter-domain federated graph classification, where the number of graph classes differs across domains. As a consequence, client-specific classifiers cannot be directly shared. Instead, we only share the GNN backbone responsible for generating graph representations.
+
+Compared with general federated learning, federated graph learning is tailored to graph-structured data and faces several unique challenges, especially when handling graphs from different domains. The key challenges include:
+
+1. Graph heterogeneity: Graph data across clients can differ substantially in both structural patterns and feature distributions, making GNN training and optimization more challenging.
+
+2. Label imbalance: Graph data on different clients often exhibit highly imbalanced class distributions, which degrades local performance and weakens the generalization ability.
+
+3. Inter-domain generalization: Clients process graph data from diverse domains and at different scales, requiring local GNN models to capture domain-invariant graph features that generalize well across clients.
+
+In FGL, the graph data on each client are drawn from a local distribution $\mathcal{D}_i$, which is shifted from a mixture of an unknown distribution $\mathcal{D}$, i.e., $\mathcal{D}_i \neq \mathcal{D}$. Consequently, graph datasets exhibit distinct statistical properties across different clients $i$ and $j$ ($i \neq j$), implying $\mathcal{D}_i \neq \mathcal{D}_j$ and leading to graph data heterogeneity. Moreover, with an ERM-based optimizer, the $i$-th client typically converges to a local minimizer $w_i^*$ that fits its local distribution $\mathcal{D}_i$ to obtain the lowest loss, but this minimizer often lies in a region with a sharp loss surface [@{CCS17+}], leading to poor generalization of the local GNN model $f_i$. To improve the generalization of as many local GNN models as possible and better accommodate out-of-distribution graph data, we equip each client with an adaptive SAM optimizer.
+
+Specifically, the SAM optimizer aims to locate flat regions of the loss landscape with uniformly low loss values by introducing small perturbations to the parameters of local GNN models, thereby exploiting the connection between loss surface geometry and model generalization. It has been shown, both theoretically and empirically, to outperform standard ERM-based optimizers [@{FKM21+}; @{KMN17+}]. Leveraging the linear property of the FGL objective in Eq. [\[eq:eq3\]](#eq:eq3){reference-type="eqref" reference="eq:eq3"}, we propose SEAL, a sharpness-aware federated graph learning algorithm that applies SAM to each client to minimize its perturbed loss. The optimization problem of SEAL is formulated as $$\begin{equation}
+ \min_{ (w_1, \cdots, w_N) } \max_{\parallel A_{w_i}^{-1} \epsilon_i \parallel_2^2 \leq \rho} \frac{1}{N} \sum_{i \in [N]} \left( \mathcal{L}_i (\tilde{w}_i) + \frac{\lambda}{2} \parallel w_i \parallel_2^2 \right), \label{eq:eq5}
+\end{equation}$$ where $\tilde{w}_i = w_i + \epsilon_i$, $\epsilon_i$ is an adversarial perturbation of $w_i$, $\rho$ is the perturbation radius (a predefined hyper-parameter), $\lambda$ is the weight decay coefficient, and $\parallel \cdot \parallel_2$ denotes the $L 2$ norm.
+
+Note that $\left\{ A_{w_i}, w_i \in \mathbb{R}^k \right\}$ denotes a family of invertible linear operators on $\mathbb{R}^k$. The operator $A_{w_i}^{-1}$ serves as a normalization operator for $w_i$, designed to address the scale dependence of sharpness [@{DPB17+}], so that it is invariant to any scaling transformation. Suppose $S$ is an invertible scaling operator on $\mathbb{R}^k$ that leaves the loss function unchanged, i.e., $\mathcal{L}_i (w_i) = \mathcal{L}_i (S w_i)$. On client $i$, for an invertible linear operator $A_{w_i}$ and a given weight vector $w_i$, we have $A_{S w_i}^{-1} S = A_{w_i}^{-1}$. Thus, the loss values at $w_i$ and $S w_i$ are identical. However, according to the sharpness definition in [@{FKM21+}], the sharpness at $w_i$ and $S w_i$ can differ arbitrarily, i.e., $$\begin{equation}
+\begin{aligned}
+\max_{\parallel \epsilon_i \parallel_2^2 \leq \rho} \mathcal{L}_i &(w_i + \epsilon_i) - \mathcal{L}_i (w_i) \\
+&\neq \max_{\parallel \epsilon_i \parallel_2^2 \leq \rho} \mathcal{L}_i (S w_i + \epsilon_i) - \mathcal{L}_i (S w_i), \label{eq:tmp1}
+\end{aligned}
+\end{equation}$$ which weakens the correlation between a model's generalization gap and its sharpness.
+
+To this end, we introduce an adaptive sharpness to train local GNN models as suggested in [@{KKP21+}]. The adaptive sharpness at $w_i$ is defined as: $\max_{\parallel A_{w_i}^{-1} \epsilon_i \parallel_2^2 \leq \rho} \mathcal{L}_i (w_i + \epsilon_i) - \mathcal{L}_i (w_i)$. At points $w_i$ and $S w_i$, this adaptive sharpness is identical, i.e., $$\begin{equation}
+\begin{aligned}
+\max_{\parallel A_{w_i}^{-1} \epsilon_i \parallel_2^2 \leq \rho} \mathcal{L}_i &(w_i + \epsilon_i) - \mathcal{L}_i (w_i) \\
+&= \max_{\parallel A_{S w_i}^{-1} \epsilon_i \parallel_2^2 \leq \rho} \mathcal{L}_i (S w_i + \epsilon_i) - \mathcal{L}_i (S w_i). \label{eq:tmp2}
+\end{aligned}
+\end{equation}$$
+
+This scale-invariant adaptive sharpness improves the training process in weight space by adjusting maximization regions according to the parameter scale. In practice, SEAL solves the min-max problem in Eq. [\[eq:eq5\]](#eq:eq5){reference-type="eqref" reference="eq:eq5"} via a two-step procedure for each update. For a small perturbation radius $\rho$, the inner maximization in Eq. [\[eq:eq5\]](#eq:eq5){reference-type="eqref" reference="eq:eq5"} can be approximated by a linearly constrained optimization problem using a first-order Taylor expansion around $w_i$: $$\begin{equation}
+\begin{aligned}
+A_{w_i^{t}}^{-1} \epsilon_i^{t} &= \mathop{\arg\max}\limits_{ \parallel A_{w_i^{t}}^{-1} \epsilon_i^{t} \parallel_2^2 \leq \rho} \mathcal{L}_i (w_i^{t} + \epsilon_i^{t}) \\
+&\approx \mathop{\arg\max}\limits_{ \parallel A_{w_i^{t}}^{-1} \epsilon_i^{t} \parallel_2^2 \leq \rho} \left( A_{w_i^{t}}^{-1} \epsilon_i^{t} \right)^{\top} A_{w_i^{t}} \nabla \mathcal{L}_i(w_i^{t}) \\
+&= \sqrt{\rho} \frac{A_{w_i^{t}} \nabla \mathcal{L}_i(w_i^{t})}{\parallel A_{w_i^{t}} \nabla \mathcal{L}_i(w_i^{t}) \parallel_2}. \label{eq:eq6}
+\end{aligned}
+\end{equation}$$
+
+So we have: $$\begin{equation}
+\begin{aligned}
+\epsilon_i^{t} = \sqrt{\rho} \frac{A_{w_i^{t}}^2 \nabla \mathcal{L}_i(w_i^{t})}{\parallel A_{w_i^{t}} \nabla \mathcal{L}_i(w_i^{t}) \parallel_2}. \label{eq:eq7}
+\end{aligned}
+\end{equation}$$
+
+In our implementation, to stabilize the training process, we use $A_{w_i} + \gamma I_{w_i}$ instead of $A_{w_i}$, where $I_{w_i}$ is a diagonal matrix of the same size as $w_i$ with all diagonal entries equal to $1$, and $A_{w_i} = w_i$ is a copy of the current weight matrix detached from the computational graph. We take the element-wise absolute value of $A_{w_i}$ and then add $\gamma I_{w_i}$; the resulting matrix is used to rescale the gradient of the current parameter. In all experiments, we set $\gamma = 0.1$.
+
+Subsequently, for each client, the optimizer performs local gradient descent at $w_i^{t}$: $$\begin{equation}
+\begin{aligned}
+w_i^{t+1} = w_i^{t} - \eta_{l} \left( \nabla \mathcal{L}_i (w_i^{t} + \epsilon_i^{t}) + \lambda w_i^{t} \right). \label{eq:eq8}
+\end{aligned}
+\end{equation}$$
+
+From Eq. [\[eq:eq7\]](#eq:eq7){reference-type="eqref" reference="eq:eq7"} and Eq. [\[eq:eq8\]](#eq:eq8){reference-type="eqref" reference="eq:eq8"}, we observe that SEAL first identifies the perturbed point $w_i^{t} + \epsilon_i^{t}$ with the largest local loss within a fixed-radius neighborhood around $w_i^{t}$, and then performs gradient descent at $w_i^{t}$ using the gradient evaluated at $w_i^{t} + \epsilon_i^{t}$. When $A_{w_i}$ is the identity matrix, the adaptive sharpness reduces to the standard sharpness defined in [@{FKM21+}]. In this case, SEAL no longer enjoys scale invariance; we refer to this variant as **SEAL-B**, i.e., $\epsilon_i^{t} = \sqrt{\rho} \frac{\nabla \mathcal{L}_i(w_i^{t})}{\parallel \nabla \mathcal{L}_i(w_i^{t}) \parallel_2}$.
+
+The primary architectural difference among GNN variants lies in the choice of the aggregation function $\mathrm{Agg} (\cdot)$ in Eq. [\[eq:eq1\]](#eq:eq1){reference-type="eqref" reference="eq:eq1"}, as in GCN [@KW17], GraphSAGE [@HYL17], GAT [@{VCC18+}], etc. These models can be abstracted as $$\begin{equation}
+ h_v^{l+1} = \sigma \left( W_l \sum_{u \in \mathcal{N}_v}\frac{h_u^{l}}{|\mathcal{N}_v|} + B_l h_v^{l} \right), \forall l \in \left[ L \right], \label{eq:eq9}
+\end{equation}$$ where $\sum_{u \in \mathcal{N}_v} h_u^{l} / |\mathcal{N}_v|$ is the mean of the previous-layer embeddings of the neighbors of node $v$, $W_l$ and $B_l$ are trainable weight and bias matrices, respectively, and $B_l$ can be omitted.
+
+To analyze the dimensional collapse that may arise during the training process, we consider a simplified $L$-layer GNN ($L \geq 1$) without nonlinear activation functions, following DirectCLR [@{JVL22+}]. Let $h_{i, v}^{L}$ denote the final-layer representation of node $v$ produced by the first $L$ layers of the local GNN on client $i$, and let $\Pi_{i} = W_{i}^{L-1} \cdots W_{i}^{1}W_{i}^{0}$ denote the product of the corresponding $L$ weight matrices. For such a simplified GNN, Theorem [\[theo:theo1\]](#theo:theo1){reference-type="ref" reference="theo:theo1"} provides the covariance matrix of graph representations on client $i$.
+
+::: theorem
+Consider a simplified $L$-layer GNN $f_i$, where the update rule is $h_{v}^{l+1} = W^l_i \sum_{u \in \mathcal{N}_{v}} h_u^{l}$. The representation of a graph $G_{i,j}$ on client $i$ is given by $\sum_{v\in V_{i,j}} f(G_{i,j})_v = \Pi_i x^L_{i,j}$, where $x^L_{i,j} = \sum_{v\in \mathcal{V}_{i,j}} \sum_{u_{L-1} \in \mathcal{N}_{v}}\sum_{u_{L-2} \in \mathcal{N}_{u_{L-1}}} \ldots \sum_{u_0\in \mathcal{N}_{u_1}} x_{u_0}$. Accordingly, the covariance matrix of graph representations on client $i$ is $$\begin{equation}
+ \Sigma_i = \Pi_i \left( \frac{1}{M_i} \sum_{j=1}^{M_i} \left( x^L_{ i,j} - \bar{x}^L_i \right) \left( x^L_{i,j} - \bar{x}^L_i \right)^{\top} \right) \Pi_i^{\top},
+\label{eq:eq10}
+\end{equation}$$ where $\bar{x}^L_i = \frac{1}{M_i} \sum_{j=1}^{M_i} x^L_{i, j}$. []{#theo:theo1 label="theo:theo1"}
+:::
+
+The gradient flow analysis of $\Pi_{i}$ indicates that data heterogeneity leads to a significant number of singular values of $\Pi_{i}$ being $0$, which makes $\Pi_{i}$ tend toward a low-rank matrix [@{SLZ23+}]. In FGL, severe data heterogeneity often induces extreme imbalance in the number of local training graph samples per class on each client [^2], so that certain classes may have a vanishingly small (or even zero) proportion of samples. This further aggravates the rank reduction of $\Pi_{i}$, leading to dimensional collapse in the resulting graph representations. As shown in Theorem [\[theo:theo1\]](#theo:theo1){reference-type="ref" reference="theo:theo1"}, once $\Pi_{i}$ becomes low-rank, the covariance matrix $\Sigma_{i}$ also tends toward low-rank, which results in severe dimensional collapse in local GNN models.
+
+:::: algorithm
+::: algorithmic
+Initial backbone parameters of local models $w_{i}^{0}$, perturbation radius $\rho$, local learning rate $\eta_{l}$, number of local epochs $E$, number of communication rounds $T$, regularization coefficient $\alpha$, hidden dimension $d$. Initialize local backbones with the global backbone: $w_{i, 0}^{t} = w^{t}, \forall i \in [N]$. Sample a batch of local data $\xi_i$ and calculate the local loss $\mathcal{L}_i(w_{i, e}^{t}; \xi_{i}) + \alpha \parallel C_i \parallel_{F}^2 / {d^2}$. Calculate $\nabla_{w_{i, e}} \left( \mathcal{L}_i(w_{i, e}^{t}; \xi_{i}) + \alpha \parallel C_i \parallel_{F}^2 / {d^2} \right)$ to obtain the local gradients and perform the gradient perturbation by Eq. [\[eq:eq7\]](#eq:eq7){reference-type="eqref" reference="eq:eq7"}. Update the local model according to Eq. [\[eq:eq8\]](#eq:eq8){reference-type="eqref" reference="eq:eq8"}. Aggregate local backbones on the server: $w^{t+1} = \sum_{i=1}^N \frac{M_i}{M} w_{i}^{t}$.
+:::
+::::
+
+To alleviate this issue, we introduce a regularization term that decorrelates graph representations during local training process. Directly regularizing the covariance matrix $\Sigma_i$ would require calculating all of its singular values, which is computationally expensive. Instead, we first perform $z$-score normalization on all graph representations $h_{i,j}$: $$\begin{equation}
+ \hat{h}_{i, j} = (h_{i, j} - \bar{h}_i) / \sqrt{\text{Var}(h_i)}. \label{eq:eq11}
+\end{equation}$$
+
+We then compute the covariance matrix of the normalized representations, denoted as $C_i$. Finally, we add the following regularization term to the local objective: $$\begin{equation}
+ \min_{w_i} \mathcal{L}_i(w_i, \xi_i) + \frac{\alpha}{d^2} \parallel C_i \parallel_{F}^2, \label{eq:eq12}
+\end{equation}$$ where $\alpha > 0$ is a regularization coefficient, $d$ is the representation dimension, and $\lVert \cdot \rVert_{F}$ denotes the Frobenius norm, which is computationally efficient. The overall training procedure of SEAL is summarized in Algorithm [\[algo:algo1\]](#algo:algo1){reference-type="ref" reference="algo:algo1"}.