GenAI4ELab/papercli-papers huggingface dataset:
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness
Authors: Joel Dapello; Kohitij Kar; Martin Schrimpf; Robert Baldwin Geary; Michael Ferguson; David Daniel Cox; James J. DiCarlo
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: While some state-of-the-art artificial neural network systems in computer vision are strikingly accurate models of the corresponding primate visual processing, there are still many discrepancies between these models and the behavior of primates on object recognition tasks. Many current models suffer from extreme sensitivity to adversarial attacks and often do not align well with the image-by-image behavioral error patterns observed in humans. Previous research has provided strong evidence that primate object recognition behavior can be very accurately predicted by neural population activity in the inferior temporal (IT) cortex, a brain area in the late stages of the visual processing hierarchy. Therefore, here we directly test whether making the late stage representations of models more similar to that of macaque IT produces new models that exhibit more robust, primate-like behavior. We conducted chronic, large-scale multi-electrode recordings  across the IT cortex in six non-human primates (rhesus macaques). We then use these data to fine-tune (end-to-end) the model "IT" representations such that they are more aligned with the biological IT representations, while preserving accuracy on object recognition tasks. We generate a cohort of models with a range of IT similarity scores validated on held-out animals across two image sets with distinct statistics. Across a battery of optimization conditions, we observed a strong correlation between the models' IT-likeness and alignment with human behavior, as well as an increase in its adversarial robustness. We further assessed the limitations of this approach and find that the improvements in behavioral alignment and adversarial robustness generalize across different image statistics, but not to object categories outside of those covered in our IT training set. Taken together, our results demonstrate that building models that are more aligned with the primate brain leads to more robust and human-like behavior, and call for larger neural data-sets to further augment these gains.
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Authors: Xiaojun Guo; Yifei Wang; Tianqi Du; Yisen Wang
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance degenerates as the layer goes deeper. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective dimensional collapse in which representations lie in a narrow cone. Accordingly, inspired by the power of contrastive learning in preventing dimensional collapse, we propose a novel normalization layer ContraNorm. Intuitively, ContraNorm implicitly shatters representations in the embedding space, leading to a more uniform distribution and slighter dimensional collapse. On the theoretical analysis, we prove that ContraNorm can alleviate both complete collapse and dimensional collapse under some conditions. Our proposed normalization layer can be easily inserted into GNNs and Transformers with negligible parameter overhead. Experiments on various real-world datasets verify the effectiveness of our method.
Sufficient Subgraph Embedding Memory for Continual Graph Representation Learning
Authors: Xikun ZHANG; Dongjin Song; Dacheng Tao
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Memory replay, which constructs a buffer to store representative samples and retrain the model over the buffer to maintain its performance over existing tasks, has shown great success for continual learning with Euclidean data. Directly applying it to graph data, however, can lead to the memory explosion problem due to the necessity to consider explicit topological connections of representative nodes. To this end, we present Parameter Decoupled Graph Neural Networks (PDGNNs) with Sufficient Subgraph Embedding Memory (SSEM) to fully utilize the explicit topological information for memory replay and reduce the memory space complexity from $\mathcal{O}(nd^L)$ to $\mathcal{O}(n)$, where $n$ is the memory buffer size, $d$ is the average node degree, and $L$ is the range of neighborhood aggregation. Specifically, PDGNNs decouple trainable parameters from the computation subgraphs via $\textit{Sufficient Subgraph Embeddings}$ (SSEs), which compress subgraphs into vectors ($\textit{i.e.}$, SSEs) to reduce the memory consumption. Besides, we discover a $\textit{pseudo-training effect}$ in memory based continual graph learning, which does not exist in continual learning on Euclidean data without topological connection ($\textit{e.g.}$, individual images). Based on the discovery, we develop a novel $\textit{coverage maximization sampling}$ strategy to enhance the performance when the memory budget is tight. Thorough empirical studies demonstrate that PDGNNs with SSEM outperform state-of-the-art techniques for both class-incremental and task-incremental settings.
Human Motion Diffusion Model
Authors: Guy Tevet; Sigal Raab; Brian Gordon; Yoni Shafir; Daniel Cohen-Or; Amit Haim Bermano
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Natural and expressive human motion generation is the holy grail of computer animation.
It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it. Therefore, current generative solutions are either low-quality or limited in expressiveness. 
Diffusion models are promising candidates for the human motion domain since they
have already shown remarkable generative capabilities in other domains, and their many-to-many nature. 
In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for human motion data.  MDM is transformer-based, combining insights from motion generation literature. 
A notable design-choice is that it predicts the sample itself rather than the noise in each step to facilitate the use of established geometric losses on the locations and velocities of the motion, such as the foot contact loss. As we demonstrate, MDM is a generic approach, enabling different modes of conditioning, and different generation tasks. We show that our model is trained with lightweight resources and yet achieves state-of-the-art results on leading benchmarks for text-to-motion, action-to-motion, and unconditioned motion generation.
Extreme Q-Learning: MaxEnt RL without Entropy
Authors: Divyansh Garg; Joey Hejna; Matthieu Geist; Stefano Ermon
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by \emph{10+ points} on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website.
Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Authors: Minghao Guo; Veronika Thost; Samuel Song; Adithya Balachandran; Payel Das; Jie Chen; Wojciech Matusik
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the wealth of recent literature. Still, these techniques are faced with a common challenge in practice: Labeled data are limited by the cost of manual extraction from literature and laborious experimentation. In this work, we propose a data-efficient property predictor by utilizing a learnable hierarchical molecular grammar that can generate molecules from grammar production rules. Such a grammar induces an explicit geometry of the space of molecular graphs, which provides an informative prior on molecular structural similarity. The property prediction is performed using graph neural diffusion over the grammar-induced geometry. On both small and large datasets, our evaluation shows that this approach outperforms a wide spectrum of baselines, including supervised and pre-trained graph neural networks. We include a detailed ablation study and further analysis of our solution, showing its effectiveness in cases with extremely limited data (only ${\sim}100$ samples), and its extension to application in molecular generation.
Hybrid Neuro-Symbolic Reasoning based on Multimodal Fusion
Authors: Subrata Das; Bodong Zhou
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Deep neural models and symbolic Artificial Intelligence (AI) systems have contrasting advantages and disadvantages. Neural models can be trained from raw, incomplete and noisy data to obtain abstraction of features at various levels, but their uninterpretability is well-known. On the other hand, the traditional rule-based symbolic reasoning encodes domain knowledge, but its failure is often attributed to the acquisition bottleneck. We propose to build a hybrid learning and reasoning system which is based on multimodal fusion approach that brings together
advantageous features from both the paradigms. Specifically, we enhance convolutional neural networks (CNNs) with the structured information of ‘if-then’ symbolic logic rules obtained via word embeddings corresponding to propositional symbols and terms. With many dozens of intuitive rules relating the type of a scene with its typical constituent objects, we are able to achieve significant improvement over the base CNN-based classification. Our approach is extendible to handle first-order logical syntax for rules and other deep learning models.
Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets
Authors: Hayeon Lee; Sohyun An; Minseon Kim; Sung Ju Hwang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture that obtains the best performance and/or efficiency when distilling the knowledge from a given teacher model. Previous DaNAS methods have mostly tackled the search for the neural architecture for fixed datasets and the teacher, which are not generalized well on a new task consisting of an unseen dataset and an unseen teacher, thus need to perform a costly search for any new combination of the datasets and the teachers. For standard NAS tasks without KD, meta-learning-based computationally efficient NAS methods have been proposed, which learn the generalized search process over multiple tasks (datasets) and transfer the knowledge obtained over those tasks to a new task. However, since they assume learning from scratch without KD from a teacher, they might not be ideal for DaNAS scenarios. To eliminate the excessive computational cost of DaNAS methods and the sub-optimality of rapid NAS methods, we propose a distillation-aware meta-accuracy prediction model, DaSS (Distillation-aware Student Search), which can predict a given architecture's final performances on a dataset when performing KD with a given teacher, without having actually to train it on the target task. The experimental results demonstrate that our proposed meta-prediction model successfully generalizes to multiple unseen datasets for DaNAS tasks, largely outperforming existing meta-NAS methods and rapid NAS baselines. Code is available at https://github.com/CownowAn/DaSS.
Temporally-Weighted Spike Encoding for Event-based Object Detection and Classification
Authors: Nikolaus Salvatore; Justin Fletcher
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Event-based cameras exhibit high dynamic range and temporal precision that could make them ideal for detecting objects with high speeds and low relative luminance. These properties have made event-based cameras especially interesting for use in space domain awareness tasks, such as detecting dim, artificial satellites with high brightness backgrounds using ground-based optical sensors; however, the asynchronous nature of event-based data presents new challenges to performing objection detection. While spiking neural networks (SNNs) have been shown to naturally complement the asynchronous and binary properties of event-based data, they also present a number of challenges in their training, such as the spike vanishing problem and the large number of timesteps required for maximizing classification and detection accuracy. Furthermore, the extremely high sampling rate of event-based sensors and the density of noisy space-based data collections can results in excessively large event streams within a short window of recording. We present a temporally-weighted spike encoding that greatly reduces the number of spikes derived from an event-based data stream, enabling the training of larger SNNs with fewer timesteps for maximal accuracy. We propose using this spike encoding with a variant of convolutional SNN trained utilizing surrogate spiking neuron gradients with backpropagation-through-time (BPTT) for both classification and object detection tasks with an emphasis on space-domain awareness. To demonstrate the efficacy of our encoding and SNN approach, we present competitive classification accuracies on benchmark datasets N-MNIST (99.7%), DVS-CIFAR10 (74.0%), and N-Caltech101 (72.8%), as well as state-of-the-art object detection performance on event-based, satellite collections.
Iterative Circuit Repair Against Formal Specifications
Authors: Matthias Cosler; Frederik Schmitt; Christopher Hahn; Bernd Finkbeiner
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We present a deep learning approach for repairing sequential circuits against formal specifications given in linear-time temporal logic (LTL). Given a defective circuit and its formal specification, we train Transformer models to output circuits that satisfy the corresponding specification. We propose a separated hierarchical Transformer for multimodal representation learning of the formal specification and the circuit. We introduce a data generation algorithm that enables generalization to more complex specifications and out-of-distribution datasets. In addition, our proposed repair mechanism significantly improves the automated synthesis of circuits from LTL specifications with Transformers. It improves the state-of-the-art by $6.8$ percentage points on held-out instances and $11.8$ percentage points on an out-of-distribution dataset from the annual reactive synthesis competition.
ATTRIBUTES RECONSTRUCTION IN HETEROGENEOUS NETWORKS VIA GRAPH AUGMENTATION
Authors: yixuan Liang; yuan wan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Heterogeneous Graph Neural Networks(HGNNs), as an effective tool for mining heterogeneous graphs, have achieved remarkable performance on node classification tasks. Yet, HGNNs are limited in their mining power as they require all nodes to have complete and reliable attributes. It is usually unrealistic since the attributes of many nodes in reality are inevitably missing or defective. Existing methods usually take imputation schemes to complete missing attributes, in which topology information is ignored, leading to suboptimal performance. And some graph augmentation techniques have improved the quality of attributes, while few of them are designed for heterogeneous graphs. In this work, we study the data augmentation on heterogeneous graphs, tackling the missing and defective attributes simultaneously, and propose a novel generic architecture—Attributes Reconstruction in Heterogeneous networks via Graph Augmentation(ARHGA), including random sampling, attribute augmentation and consistency training. In graph augmentation, to ensure attributes plausible and accurate, the attention mechanism is adopted to reconstruct attributes under the guidance of the topological relationship between nodes. Our proposed architecture can be easily combined with any GNN-based heterogeneous model, and improves the performance. Extensive experiments on three benchmark datasets demonstrate the superior performance of ARHGA over strate-of-the-art baselines on semi-supervised node classification.
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
Authors: Stone Tao; Xiaochen Li; Tongzhou Mu; Zhiao Huang; Yuzhe Qin; Hao Su
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Training long-horizon robotic policies in complex physical environments is essential for many applications, such as robotic manipulation. However, learning a policy that can generalize to unseen tasks is challenging. In this work, we propose to achieve one-shot task generalization by decoupling plan generation and plan execution. Specifically, our method solves complex long-horizon tasks in three steps: build a paired abstract environment by simplifying geometry and physics, generate abstract trajectories, and solve the original task by an abstract-to-executable trajectory translator. In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate. However, this introduces a large domain gap between abstract trajectories and the actual executed trajectories as abstract trajectories lack low-level details and aren’t aligned frame-to-frame with the executed trajectory. In a manner reminiscent of language translation, our approach leverages a seq-to-seq model to overcome the large domain gap between the abstract and executable trajectories, enabling the low-level policy to follow the abstract trajectory. Experimental results on various unseen long-horizon tasks with different robot embodiments demonstrate the practicability of our methods to achieve one-shot task generalization. Videos and more details can be found in the supplementary materials and project page: https://sites.google.com/view/abstract-to-executable-iclr23/
Joint-Predictive Representations for Multi-Agent Reinforcement Learning
Authors: Mingxiao Feng; Wengang Zhou; Yaodong Yang; Houqiang Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The recent advances in reinforcement learning have demonstrated the effectiveness of vision-based self-supervised learning (SSL). However, the main efforts on this direction have been paid on single-agent setting, making multi-agent reinforcement learning~(MARL) lags thus far. There are two significant obstacles that prevent applying off-the-shelf SSL approaches with MARL on a partially observable multi-agent system : (a) each agent only gets a partial observation, and (b) previous SSL approaches only take consistent temporal representations into account, while ignoring the characterization that captures the interaction and fusion among agents. In this paper, we propose \textbf{M}ulti-\textbf{A}gent  \textbf{Jo}int-Predictive \textbf{R}epresentations~(MAJOR), a novel framework to explore self-supervised learning on cooperative MARL. Specifically, we treat the latent representations of local observations of all agents as the sequence of masked contexts of the global state, and we then learn effective representations by predicting the future latent representations for each agent with the help of the agent-level information interactions in a joint transition model. We have conducted extensive experiments on wide-range MARL environments, including both vision-based and state-based scenarios, and show that our proposed MAJOR achieves superior asymptotic performance and sample efficiency against other state-of-the-art methods.
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
Authors: Jun Zhang; Shuyang Jiang; Jiangtao Feng; Lin Zheng; Lingpeng Kong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Transformer has achieved remarkable success in language, image, and speech processing. Recently, various efficient attention architectures have been proposed to improve transformer's efficiency while largely preserving its efficacy, especially in modeling long sequences. A widely-used benchmark to test these efficient methods' capability on long-range modeling is Long Range Arena (LRA). However, LRA only focuses on the standard bidirectional (or noncausal) self attention, and completely ignores cross attentions and unidirectional (or causal) attentions, which are equally important to downstream applications. Although designing cross and causal variants of an attention method is straightforward for vanilla attention, it is often challenging for efficient attentions with subquadratic time and memory complexity. In this paper, we propose Comprehensive Attention Benchmark (CAB) under a fine-grained attention taxonomy with four distinguishable attention patterns, namely, noncausal self, causal self, noncausal cross, and causal cross attentions. CAB collects seven real-world tasks from different research areas to evaluate efficient attentions under the four attention patterns. Among these tasks, CAB validates efficient attentions in eight backbone networks to show their generalization across neural architectures. We conduct exhaustive experiments to benchmark the performances of nine widely-used efficient attention architectures designed with different philosophies on CAB. Extensive experimental results also shed light on the fundamental problems of efficient attentions, such as efficiency length against vanilla attention, performance consistency across attention patterns, the benefit of attention mechanisms, and interpolation/extrapolation on long-context language modeling.
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
Authors: Nihal V. Nayak; Peilin Yu; Stephen Bach
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., old cat and young tiger). VLMs have a flexible text encoder that can represent arbitrary classes as natural language prompts but they often underperform task-specific architectures on the compositional zero-shot benchmark datasets. CSP treats the attributes and objects that define classes as learnable tokens of vocabulary. During training, the vocabulary is tuned to recognize classes that compose tokens in multiple ways (e.g., old cat and white cat). At test time, we recompose the learned attribute-object vocabulary in new combinations to recognize novel classes. We show that CSP outperforms the CLIP on benchmark datasets by an average of 10.9 percentage points on AUC. CSP also outperforms CoOp, a soft prompting method that fine-tunes the prefix context tokens, by an average of 5.8 percentage points on AUC. We perform additional experiments to show that CSP improves generalization to higher-order attribute-attribute-object compositions (e.g., old white cat) and combinations of pretrained attributes and fine-tuned objects. The code is available at https://github.com/BatsResearch/csp.