GenAI4ELab/papercli-papers huggingface dataset:
VA-DepthNet: A Variational Approach to Single Image Depth Prediction
Authors: Ce Liu; Suryansh Kumar; Shuhang Gu; Radu Timofte; Luc Van Gool
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using classical first-order variational constraints for this problem. While state-of-the-art deep neural network methods for SIDP learn the scene depth from images in a supervised setting, they often overlook the invaluable invariances and priors in the rigid scene space, such as the regularity of the scene. The paper's main contribution is to reveal the benefit of classical and well-founded variational constraints in the neural network design for the SIDP task. It is shown that imposing first-order variational constraints in the scene space together with popular encoder-decoder-based network architecture design provides excellent results for the supervised SIDP task. The imposed first-order variational constraint makes the network aware of the depth gradient in the scene space, i.e., regularity. The paper demonstrates the usefulness of the proposed approach via extensive evaluation and ablation analysis over several benchmark datasets, such as KITTI, NYU Depth V2, and SUN RGB-D. The VA-DepthNet at test time shows considerable improvements in depth prediction accuracy compared to the prior art and is accurate also at high-frequency regions in the scene space.  At the time of writing this paper, our method---labeled as VA-DepthNet, when tested on the KITTI depth-prediction evaluation set benchmarks, shows state-of-the-art results, and is the top-performing published approach.
Representational Dissimilarity Metric Spaces for Stochastic Neural Networks
Authors: Lyndon Duong; Jingyang Zhou; Josue Nassar; Jules Berman; Jeroen Olieslagers; Alex H Williams
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Quantifying similarity between neural representations---e.g. hidden layer activation vectors---is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of _deterministic_ representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al. 2021) to quantify differences in _stochastic_ representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space.
Open-domain Visual Entity Linking
Authors: Hexiang Hu; Yi Luan; Urvashi Khandelwal; Mandar Joshi; Kenton Lee; Kristina Toutanova; Ming-Wei Chang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We introduce the task of Open-domain Visual Entity Linking (OVEN), targeting a wide range of entities including animals, plants, buildings, locations and much more. Given an image (e.g., an image of an aircraft), a text query (`What is the model?' or `What is the airline?'), and a multi-modal knowledge base (e.g., Wikipedia),  the goal is to link to an entity  (Boeing-777 or EVA Air) out of all entities in the knowledge base. We build a benchmark dataset (OVEN-wiki), by repurposing 14 existing image classification, image retrieval, and visual QA datasets. We link all existing labels to Wikipedia entities when possible, using a state-of-the-art entity linking system and human annotators, creating a diverse and unified label space. OVEN is a rich and challenging task, which requires models to recognize and link visual content to both a small set of seen entities as well as a much larger set of unseen entities (e.g., unseen aircraft models). OVEN also requires models to generalize to previously unseen intents that may require more fine-grained reasoning (`Who manufactured the aircraft in the back?'). We build strong baselines based on state-of-the-art pre-trained models and find that current pre-trained models struggle to address the challenges posed by OVEN. We hope OVEN will inspire next-generation pre-training techniques and pave the way to future knowledge-intensive vision tasks.
Representation Balancing with Decomposed Patterns for Treatment Effect Estimation
Authors: Yiyan HUANG; WANG Siyi; Cheuk Hang LEUNG; Qi WU; Dongdong WANG; Zhixiang Huang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Estimating treatment effects from observational data is subject to a problem of covariate shift caused by selection bias. Recent studies have attempted to mitigate this problem by group distance minimization, that is, balancing the distribution of representations between the treated and controlled groups. The rationale behind this is that learning balanced representations while preserving the predictive power of factual outcomes is expected to generalize to counterfactual inference. Inspired by this, we propose a new approach to better capture the patterns that contribute to representation balancing and outcome prediction. Specifically, we derive a theoretical bound that naturally ties the notion of propensity confusion to representation balancing, and further transform the balancing Patterns into Decompositions of Individual propensity confusion and Group distance minimization (PDIG). Moreover, we propose to decompose proxy features into Patterns of Pre-balancing and Balancing Representations (PPBR), as it is insufficient if only balanced representations are considered in outcome prediction. Extensive experiments on simulation and benchmark data confirm not only PDIG leads to mutual reinforcement between individual propensity confusion and group distance minimization, but also PPBR brings improvement to outcome prediction, especially counterfactual inference. We believe these findings are heuristics for further investigation of what affects the generalizability of representation balancing models in counterfactual estimation.
Clustering and Ordering Variable-Sized Sets: The Catalog Problem
Authors: Mateusz Maria Jurewicz; Graham W. Taylor; Leon Derczynski
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Prediction of a varying number of ordered clusters from sets of any cardinality is a challenging task for neural networks, combining elements of set representation, clustering and learning to order. This task arises in many diverse areas, ranging from medical triage, through multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This paper focuses on the latter, which exemplifies a number of challenges inherent to adaptive ordered clustering, referred to further as the eponymous Catalog Problem. These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity. Despite progress in both neural clustering and set-to-sequence methods, no joint, fully differentiable model exists to-date. We develop such a modular architecture, referred to further as Neural Ordered Clusters (NOC), enhance it with a specific mechanism for learning cluster-level cardinality constraints, and provide a robust comparison of its performance in relation to alternative models. We test our method on three datasets, including synthetic catalog structures and PROCAT, a dataset of real-world catalogs consisting of over 1.5M products, achieving state-of-the-art results on a new, more challenging formulation of the underlying problem, which has not been addressed before. Additionally, we examine the network's ability to learn higher-order interactions and investigate its capacity to learn both compositional and structural rulesets.
Actionable Neural Representations: Grid Cells from Minimal Constraints
Authors: Will Dorrell; Peter E. Latham; Timothy E. J. Behrens; James C. R. Whittington
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent meaning of actions across space, as it allows you to find new short-cuts and navigate in unfamiliar settings. We term this representation an  `actionable representation'. We formulate actionable representations using group and representation theory, and show that, when combined with biological and functional constraints - non-negative firing, bounded neural activity, and precise coding - multiple modules of hexagonal grid cells are the optimal representation of 2D space. We support this claim with intuition, analytic justification, and simulations. Our analytic results normatively explain a set of surprising grid cell phenomena, and make testable predictions for future experiments. Lastly, we highlight the generality of our approach beyond just understanding 2D space. Our work characterises a new principle for understanding and designing flexible internal representations: they should be actionable, allowing animals and machines to predict the consequences of their actions, rather than just encode.
Set-Level Self-Supervised Learning from Noisily-Labeled Data
Authors: Chia-Ching Lin; Shang-Fu Chen; Fu-En Yang; Yu-Chiang Frank Wang; Chin-Laung Lei
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Noisy labels are inevitably presented in real-world datasets due to labeling error or visual content ambiguity. Existing methods generally approach the task of noisy label learning (NLL) by either properly regularizing the model, or reweighting clean/noisy labeled samples. While self-supervised learning (SSL) has been applied to pre-train deep neural networks without label supervision, downstream tasks like image classification still require clean labeled data. And, most SSL strategies are performed at the instance level, regardless of the correctness of its label. In this paper, we propose set-level self-supervised learning (SLSSL), which performs SSL at mini-batch levels with observed noisy labels. By corrupting the labels of each training mini-batch, our SLSSL enforces the model to exhibit sufficient robustness. Moreover, the proposed SLSSL can also be utilized for sample reweighting technique. As a result, the proposed learning scheme can be applied as an expectation-maximization (EM) algorithm during model training. Extensive experiments on synthetic and real-world noisy label data confirm the effectiveness of our framework.
What do Vision Transformers Learn?  A Visual Exploration
Authors: Amin Ghiasi; Hamid Kazemi; Steven Reich; Eitan Borgnia; Manli Shu; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing studies visually analyze the mechanisms of convolutional neural networks, an analogous exploration of ViTs remains challenging. In this paper, we first address the obstacles to performing visualizations on ViTs. Assisted by these solutions, we observe that neurons in ViTs trained with language model supervision (e.g., CLIP) are activated by semantic concepts rather than visual features. We also explore the underlying differences between ViTs and CNNs, and we find that transformers detect image background features, just like their convolutional counterparts, but their predictions depend far less on high-frequency information. On the other hand, both architecture types behave similarly in the way features progress from abstract patterns in early layers to concrete objects in late layers. In addition, we show that ViTs maintain spatial information in all layers except the final layer. In contrast to previous works, we show that the last layer most likely discards the spatial information and behaves as a learned global pooling operation. Finally, we conduct large-scale visualizations on a wide range of ViT variants, including DeiT, CoaT, ConViT, PiT, Swin, and Twin, to validate the effectiveness of our method.
Critical Initialization of Wide and Deep Neural Networks through Partial Jacobians: General Theory and Applications
Authors: Darshil Doshi; Tianyu He; Andrey Gromov
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep neural networks are notorious for defying theoretical treatment. However, when the number of parameters in each layer tends to infinity the network function is a Gaussian process (GP) and quantitatively predictive description is possible. Gaussian approximation allows to formulate criteria for selecting hyperparameters, such as variances of weights and biases, as well as the learning rate. These criteria rely on the notion of criticality defined for deep neural networks. In this work we describe a new practical way to diagnose criticality. We introduce *partial Jacobians* of a network, defined as derivatives of preactivations in layer $l$ with respect to preactivations in layer $l_0\leq l$. We derive recurrence relations for the norms of partial Jacobians and utilize these relations to analyze criticality of deep fully connected neural networks with LayerNorm and/or residual connections. We derive and implement a simple and cheap numerical test that allows one to select optimal initialization for a broad class of deep neural networks. Using these tools we show quantitatively that proper stacking of the LayerNorm (applied to preactivations) and residual connections leads to an architecture that is critical for any initialization. Finally, we apply our methods to analyze the MLP-Mixer architecture and show that it is everywhere critical.
Oracle-oriented Robustness: Robust Image Model Evaluation with Pretrained Models as Surrogate Oracle
Authors: Peiyan Zhang; Sunghun Kim; Eric Xing; Haohan Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning has demonstrated remarkable performances over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model’s performances in the real world is still in discussion. In reality, an ideal robust model will probably behave similarly to the oracle (*e.g.*, the human users), thus a good evaluation protocol is probably to evaluate the models’ behaviors in comparison to the oracle. In this paper, we introduce a new robustness measurement that directly measures the image classification model’s performance compared with a surrogate oracle. Besides, we design a simple method that can accomplish the evaluation beyond the scope of the benchmarks. Our method extends the image datasets with new samples that are sufficiently perturbed to be distinct from the ones in the original sets, but are still bounded within the same causal structure the original test image represents, constrained by a surrogate oracle model pretrained with a large amount of samples. As a result, our new method will offer us a new way to evaluate the models’ robustness performances, free of limitations of fixed benchmarks or constrained perturbations, although scoped by the power of the oracle. In addition to the evaluation results, we also leverage our generated data to understand the behaviors of the model and our new evaluation strategies.
Toward Adversarial Training on Contextualized Language Representation
Authors: Hongqiu Wu; Yongxiang Liu; Hanwen Shi; hai zhao; Min Zhang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pre-training where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 $ \rightarrow $ 61.1), HellaSWAG (93.0  $ \rightarrow $ 94.9), ANLI (68.1  $ \rightarrow $ 69.3).
ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning
Authors: Olga Golovneva; Moya Peng Chen; Spencer Poff; Martin Corredor; Luke Zettlemoyer; Maryam Fazel-Zarandi; Asli Celikyilmaz
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality — among other traits — by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.
Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
Authors: Edward De Brouwer; Rahul G Krishnan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Neural ordinary differential equations (Neural ODEs) are an effective framework for learning dynamical systems from irregularly sampled time series data. These models provide a continuous-time latent representation of the underlying dynamical system where new observations at arbitrary time points can be used to update the latent representation of the dynamical system. Existing parameterizations for the dynamics functions of Neural ODEs limit the ability of the model to retain global information about the time series; specifically, a piece-wise integration of the latent process between observations can result in a loss of memory on the dynamic patterns of previously observed data points. We propose PolyODE, a Neural ODE that models the latent continuous-time process as a projection onto a basis of orthogonal polynomials. This formulation enforces long-range memory and preserves a global representation of the underlying dynamical system. Our construction is backed by favourable theoretical guarantees and in a series of experiments, we demonstrate that it outperforms previous works in the reconstruction of past and future data, and in downstream prediction tasks.
Revisiting Embeddings for Graph Neural Networks
Authors: Skye Purchase; Yiren Zhao; Robert D. Mullins
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Current graph representation learning techniques use Graph Neural Networks
(GNNs) to extract features from dataset embeddings. In this work, we examine
the quality of these embeddings and assess how changing them can affect the ac-
curacy of GNNs. We explore different embedding extraction techniques for both
images and texts; and find that the choice of embedding biases the performance
of different GNN architectures and thus the choice of embedding influences the
selection of GNNs regardless of the underlying dataset. In addition, we only see
an improvement in accuracy from some GNN models compared to the accuracy of
models trained from scratch or fine-tuned on the underlying data without utilising
the graph connections. As an alternative, we propose Graph-connected Network
(GraNet) layers to better leverage existing unconnected models within a GNN.
Existing language and vision models are thus improved by allowing neighbour-
hood aggregation. This gives a chance for the model to use pre-trained weights, if
possible, and we demonstrate that this approach improves the accuracy compared
to traditional GNNs: on Flickr v2, GraNet beats GAT2 and GraphSAGE by 7.7%
and 1.7% respectively.
Waveformer: Linear-Time Attention with Forward and Backward Wavelet Transform
Authors: Yufan Zhuang; Zihan Wang; Fangbo Tao; Jingbo Shang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose Waveformer that learns attention mechanism in the wavelet coefficient space, requires only linear time complexity, and enjoys universal approximating power. Specifically, we first apply forward wavelet transform to project the input sequences to multi-resolution orthogonal wavelet bases, then conduct nonlinear transformations (in this case, a random feature kernel) in the wavelet coefficient space, and finally reconstruct the representation in input space via backward wavelet transform. We note that other non-linear transformations may be used, hence we name the learning paradigm Wavelet transformatIon for Sequence lEarning (WISE). We emphasize the importance of backward reconstruction in the WISE paradigm — without it, one would be mixing information from both the input space and coefficient space through skip connections, which shall not be considered as mathematically sound. Compared with Fourier transform in recent works, wavelet transform is more efficient in time complexity and better captures local and positional information; we further support this through our ablation studies. Extensive experiments on seven long-range understanding datasets from the Long Range Arena benchmark and code understanding tasks demonstrate that (1) Waveformer achieves competitive and even better accuracy than a number of state-of-the-art Transformer variants and (2) WISE can boost accuracies of various attention approximation methods without increasing the time complexity. These together showcase the superiority of learning attention in a wavelet coefficient space over the input space.