GenAI4ELab/papercli-papers huggingface dataset:
Laser: Latent Set Representations for 3D Generative Modeling
Authors: Pol Moreno; Adam R. Kosiorek; Heiko Strathmann; Daniel Zoran; Rosalia Galiazzi Schneider; Björn Winckler; Larisa Markeeva; Theophane Weber; Danilo Jimenez Rezende
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: NeRF provides unparalleled fidelity of novel view synthesis---rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a scene, which limits its applicability.
While these issues can be addressed by learning a prior over scenes in various forms, previous approaches have been either applied to overly simple scenes or struggling to render unobserved parts.
We introduce Laser-NV---a generative model which achieves high modelling capacity, and which is based on a set-valued latent representation modelled by normalizing flows.
Similarly to previous amortized approaches, Laser-NV learns structure from multiple scenes and is capable of fast, feed-forward inference from few views. 
To encourage higher rendering fidelity and consistency with observed views, Laser-NV further incorporates a geometry-informed attention mechanism over the observed views.
Laser-NV further produces diverse and plausible completions of occluded parts of a scene while remaining consistent with observations.
Laser-NV shows state-of-the-art novel-view synthesis quality when evaluated on ShapeNet and on a novel simulated City dataset, which features high uncertainty in the unobserved regions of the scene.
Adversarial Diversity in Hanabi
Authors: Brandon Cui; Andrei Lupu; Samuel Sokota; Hengyuan Hu; David J Wu; Jakob Nicolaus Foerster
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: Many Dec-POMDPs admit a qualitatively diverse set of ''reasonable'' joint policies, where reasonableness is indicated by symmetry equivariance, non-sabotaging behaviour and the graceful degradation of performance when paired with ad-hoc partners. Some of the work in diversity literature is concerned with generating these policies. Unfortunately, existing methods fail to produce teams of agents that are simultaneously diverse, high performing, and reasonable. In this work, we propose a novel approach, adversarial diversity (ADVERSITY), which is designed for turn-based Dec-POMDPs with public actions. ADVERSITY relies on off-belief learning to encourage reasonableness and skill, and on ''repulsive'' fictitious transitions to encourage diversity. We use this approach to generate new agents with distinct but reasonable play styles for the card game Hanabi and open-source our agents to be used for future research on (ad-hoc) coordination.
Structure-based Drug Design with Equivariant Diffusion Models
Authors: Arne Schneuing; Yuanqi Du; Charles Harris; Arian Rokkum Jamasb; Ilia Igashov; weitao Du; Tom Leon Blundell; Pietro Lio; Carla P Gomes; Max Welling; Michael M. Bronstein; Bruno Correia
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Traditional SBDD pipelines start with large-scale docking of compound libraries from public databases, thus limiting the exploration of chemical space to existent previously studied regions.
Recent machine learning methods approached this problem using an atom-by-atom generation approach, which is computationally expensive. 
In this paper, we formulate SBDD as a 3D-conditional generation problem and present DiffSBDD, an E(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets. 
Furthermore, we curate a new dataset of experimentally determined binding complex data from Binding MOAD to provide realistic binding scenario rather than the synthetic CrossDocked dataset. Comprehensive in silico experiments demonstrate the efficiency of DiffSBDD in generating novel and diverse drug-like ligands that engage protein pockets with high binding energies as predicted by in silico docking.
GFlowNets and variational inference
Authors: Nikolay Malkin; Salem Lahlou; Tristan Deleu; Xu Ji; Edward J Hu; Katie E Everett; Dinghuai Zhang; Yoshua Bengio
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions. Code: https://github.com/GFNOrg/GFN_vs_HVI.
Novel Class Discovery under Unreliable Sampling
Authors: Haoang Chi; Wenjing Yang; Feng Liu; Long Lan; Tongliang Liu; Tao Qin; Bo Han
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: When sampling data of specific classes (i.e., known classes) for a scientific task, collectors may encounter unknown classes (i.e., novel classes). Since these novel classes might be valuable for future research, collectors will also sample them and assign them to several clusters with the help of known-class data. This assigning process is also known as novel class discovery (NCD). However, sampling errors are common in practice and may make the NCD process unreliable. To tackle this problem, this paper introduces a new and more realistic setting, where collectors may misidentify known classes and even confuse known classes with novel classes - we name it NCD under unreliable sampling (NUSA). We find that NUSA will empirically degrade existing NCD methods if taking no care of sampling errors. To handle NUSA, we propose an effective solution, named hidden-prototype-based discovery network (HPDN). HPDN first trains a deep network to fully fit the wrongly sampled data, then applies the relatively clean hidden representations yielded by this network into a novel mini-batch K-means algorithm, which further prevents them overfitting to residual errors by detaching noisy supervision timely. Experiments demonstrate that, under NUSA, HPDN significantly outperforms competitive baselines (e.g., 6% more than the best baseline on CIFAR-10) and keeps robust even encountering serious sampling errors.
Distributional Reinforcement Learning via Sinkhorn Iterations
Authors: Ke Sun; Yingnan Zhao; Yi Liu; Wulong Liu; Bei Jiang; Linglong Kong
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the entire distribution of the total return rather than only its expectation.  The empirical success of distributional RL is determined by the representation of return distributions and the choice of distribution divergence. In this paper, we propose a new class of \textit{Sinkhorn distributional RL~(SinkhornDRL)} algorithm that learns a finite set of statistics, i.e., deterministic samples, from each return distribution and then uses Sinkhorn iterations to evaluate the Sinkhorn distance between the current and target Bellmen distributions. Sinkhorn divergence features as the interpolation between the Wasserstein distance and Maximum Mean Discrepancy~(MMD). SinkhornDRL finds a sweet spot by taking advantage of the geometry of optimal transport-based distance and the unbiased gradient estimate property of MMD. Finally, compared to state-of-the-art algorithms, SinkhornDRL's competitive performance is demonstrated on the suit of 55 Atari games.
Meta-Learning in Games
Authors: Keegan Harris; Ioannis Anagnostides; Gabriele Farina; Mikhail Khodak; Steven Wu; Tuomas Sandholm
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions—ranging from routing problems to online advertising auctions—evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we introduce meta-learning for equilibrium finding and learning to play games. We establish the first meta-learning guarantees for a variety of fundamental and well-studied games, including two-player zero-sum games, general-sum games, Stackelberg games, and multiple extensions thereof. In particular, we obtain rates of convergence to different game-theoretic equilibria that depend on natural notions of similarity between the sequence of games encountered, while at the same time recovering the known single-game guarantees when the sequence of games is arbitrary. Along the way, we prove a number of new results in the single-game regime through a simple and unified framework, which may be of independent interest. Finally, we evaluate our meta-learning algorithms on endgames faced by the poker agent Libratus against top human professionals. The experiments show that games with varying stack sizes can be solved significantly faster using our meta-learning techniques than by solving them separately, often by an order of magnitude.
Anatomical Structure-Aware Image Difference Graph Learning for Difference-Aware Medical Visual Question Answering
Authors: Xinyue Hu; Lin Gu; Qingyu Chen; Liangchen Liu; Kazuma Kobayashi; Qiyuan An; Zhang Mengliang; Tatsuya Harada; Zhiyong Lu; Yingying Zhu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Different Visual Question Answering (VQA) task. Given a pair of main and reference images, this task attempts to answer several questions on both diseases and more importantly, the differences between them. This is consistent with the radiologist's diagnosis practice that compares the current image with the reference before concluding the report. For this task, we propose a new dataset, namely  MIMIC-Diff-VQA including 700,821 QA pairs on 109,872 pairs of images. Meanwhile, we also propose a novel expert knowledge-aware graph representation learning model to address this problem. We leveraged the expert knowledge such as anatomical structure prior, semantic and spatial knowledge to construct a multi-relationship graph to represent the image differences between two images for the image difference VQA task. Our dataset and code will be released upon publication. We believe this work would further push forward the medical vision language model.
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank
Authors: Quentin Garrido; Randall Balestriero; Laurent Najman; Yann LeCun
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid development, with the emergence of many method variations and few principled guidelines that would help practitioners to successfully deploy those methods. The main reason for that pitfall actually comes from JE-SSL's core principle of not employing any input reconstruction. Without any visual clue, it becomes extremely cryptic to judge the quality of a learned representation without having access to a labelled dataset. We hope to correct those limitations by providing a single --theoretically motivated-- criterion that reflects the quality of learned JE-SSL representations: their effective rank. Albeit simple and computationally friendly, this method ---coined {\em RankMe}--- allows one to assess the performance of JE-SSL representations, even on different downstream datasets, without requiring any labels, training or parameters to tune. Through thorough empirical experiments involving hundreds of repeated training episodes, we demonstrate how RankMe can be used for hyperparameter selection with nearly no loss in final performance compared to the current selection method that involve dataset labels. We hope that RankMe will facilitate the use of JE-SSL in domains with little or no labeled data.
GANet: Graph-Aware Network for Point Cloud Completion with Displacement-Aware Point Augmentor
Authors: Hongyu Yan; Haoxi Ran; Li Lu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Remarkably, real-world data (e.g., LiDAR-based point clouds) is commonly sparse, uneven, occluded, and truncated. The point cloud completion task draws due attention, which aims to predict a complete and accurate shape from its partial observation. However, existing methods commonly adopt PointNet or PointNet++ to extract features of incomplete point clouds. In this paper, we propose an end-to-end Graph-Aware Network (\textbf{GANet}) to effectively learn from the contour information of the partial point clouds. Moreover, we design Displacements-Aware Augmentor (DPA) to upsample and refine coarse point clouds. With our graph-based feature extractors and Displacements-Aware Transformer, our DPA can precisely capture the geometric and structural features to refine the complete point clouds. Experiments on PCN and MVP datasets demonstrate that our GANet achieves state-of-the-art on the task of shape completion.
Beyond Counting Linear Regions of Neural Networks, Simple Linear Regions Dominate!
Authors: FENGLEI FAN; Wei Huang; Lecheng Ruan; Tieyong Zeng; Huan Xiong; Fei Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Functions represented by a neural network with the widely-used ReLU activation are piecewise linear functions over linear regions (polytopes). Figuring out the properties of such polytopes is of fundamental importance for the development of neural networks.
So far, either theoretical or empirical studies on polytopes stay at the level of counting their number. Despite successes in explaining the power of depth and so on, counting the number of polytopes puts all polytopes on an equal booting, which is essentially an incomplete characterization of polytopes. Beyond counting, here we study the shapes of polytopes via the number of simplices obtained by triangulations of polytopes. First, we demonstrate the properties of the number of simplices in triangulations of polytopes, and compute the upper and lower bounds of the maximal number of simplices that a network can generate. Next, by computing and analyzing the histogram of simplices across polytopes, we find that a ReLU network has surprisingly uniform and simple polytopes, although these polytopes theoretically can be rather diverse and complicated. This finding is a novel implicit bias that concretely reveals what kind of simple functions a network learns and sheds light on why deep learning does not overfit. Lastly, we establish a theorem to illustrate why polytopes produced by a deep network are simple and uniform. The core idea of the proof is counter-intuitive: adding depth probably does not create a more complicated polytope. We hope our work can inspire more research into investigating polytopes of a ReLU neural network, thereby upgrading the knowledge of neural networks to a new level.
Decoupled Mixup for Data-efficient Learning
Authors: Zicheng Liu; Siyuan Li; Ge Wang; Cheng Tan; Lirong Wu; Stan Z. Li
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup methods have improved previous static policies effectively (e.g., linear interpolation) by maximizing salient regions or maintaining the target in mixed samples. The discrepancy is that the generated mixed samples from dynamic policies are more instance discriminative than the static ones, e.g., the foreground objects are decoupled from the background. However, optimizing mixup policies with dynamic methods in input space is an expensive computation compared to static ones. Hence, we are trying to transfer the decoupling mechanism of dynamic methods from the data level to the objective function level and propose the general decoupled mixup (DM) loss. The primary effect is that DM can adaptively focus on discriminative features without losing the original smoothness of the mixup while avoiding heavy computational overhead. As a result, DM enables static mixup methods to achieve comparable or even exceed the performance of dynamic methods. This also leads to an interesting objective design problem for mixup training that we need to focus on both smoothing the decision boundaries and identifying discriminative features. Extensive experiments on supervised and semi-supervised learning benchmarks across seven classification datasets validate the effectiveness of DM by equipping it with various mixup methods.
Understanding weight-magnitude hyperparameters in training binary networks
Authors: Joris Quist; Yunqiang Li; Jan van Gemert
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valued weights. Current BNNs use latent real-valued weights during training, where several training hyper-parameters are inherited from real-valued networks. The interpretation of several of these hyperparameters is based on the magnitude of the real-valued weights. For BNNs, however, the magnitude of binary weights is not meaningful, and thus it is unclear what these hyperparameters actually do. One example is weight-decay, which aims to keep the magnitude of real-valued weights small. Other examples are latent weight initialization, the learning rate, and learning rate decay, which influence the magnitude of the real-valued weights. The magnitude is interpretable for real-valued weights, but loses its meaning for binary weights. In this paper we offer a new interpretation of these magnitude-based hyperparameters based on higher-order gradient filtering during network optimization. Our analysis makes it possible to understand how magnitude-based hyperparameters influence the training of binary networks which allows for new optimization filters specifically designed for binary neural networks that are independent of their real-valued interpretation. Moreover, our improved understanding reduces the number of hyperparameters, which in turn eases the hyperparameter tuning effort which may lead to better hyperparameter values for improved accuracy. Code is available at https://github.com/jorisquist/Understanding-WM-HP-in-BNNs
Perturbation Analysis of Neural Collapse
Authors: Tom Tirer; Haoxiang Huang; Jonathan Niles-Weed
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Training deep neural networks for classification often includes minimizing the training loss beyond the zero training error point. In this phase of training, a “neural collapse” behavior has been observed: the variability of features (outputs of the penultimate layer) of within-class samples decreases and the mean features of different classes approach a certain tight frame structure. Recent works analyze this behavior via idealized unconstrained features models where all the minimizers exhibit exact collapse. However, with practical networks and datasets, the features typically do not reach exact collapse, e.g., because deep layers cannot arbitrarily modify intermediate features that are far from being collapsed. In this paper, we propose a richer model that can capture this phenomenon by forcing the features to stay in the vicinity of a predefined features matrix (e.g., intermediate features). We explore the model in the small vicinity case via perturbation analysis and establish results that cannot be obtained by the previously studied models. For example, we prove reduction in the within-class variability of the optimized features compared to the predefined input features (via analyzing gradient flow on the “central-path” with minimal assumptions), analyze the minimizers in the near-collapse regime, and provide insights on the effect of regularization hyperparameters on the closeness to collapse. We support our theory with experiments in practical deep learning settings.
Efficient, Stable, and Analytic Differentiation of the Sinkhorn Loss
Authors: Yixuan Qiu; Haoyun Yin; Xiao Wang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Optimal transport and the Wasserstein distance have become indispensable building blocks of modern deep generative models, but their computional costs greatly prohibit their applications in statistical machine learning models. Recently, the Sinkhorn loss, as an approximation to the Wasserstein distance, has gained massive popularity, and much work has been done for its theoretical properties. To embed the Sinkhorn loss into gradient-based learning frameworks, efficient algorithms for both the forward and backward passes of the Sinkhorn loss are required. In this article, we first demonstrate issues of the widely-used Sinkhorn's algorithm, and show that the L-BFGS algorithm is a potentially better candidate for the forward pass. Then we derive an analytic form of the derivative of the Sinkhorn loss with respect to the input cost matrix, which results in a very efficient backward algorithm. We rigorously analyze the convergence and stability properties of the advocated algorithms, and use various numerical experiments to validate the superior performance of the proposed methods.
Learning Combinatorial Node Labeling Algorithms
Authors: Lukas Gianinazzi; Maximilian Fries; Nikoli Dryden; Tal Ben-Nun; Maciej Besta; Torsten Hoefler
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We present the combinatorial node labeling framework, which generalizes many prior approaches to solving hard graph optimization problems by supporting problems where solutions consist of arbitrarily many node labels, such as graph coloring. We then introduce a neural network architecture to implement this framework. Our architecture builds on a graph attention network with several inductive biases to improve solution quality and is trained using policy gradient reinforcement learning. We demonstrate our approach on both graph coloring and minimum vertex cover. Our learned heuristics match or outperform classical hand-crafted greedy heuristics and machine learning approaches while taking only seconds on large graphs. We conduct a detailed analysis of the learned heuristics and architecture choices and show that they successfully adapt to different graph structures.
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
Authors: Pedro Pinto Santos; Diogo S. Carvalho; Miguel Vasco; Francisco S. Melo; Alberto Sardinha; Pedro A. Santos; Ana Paiva
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully perform cooperative tasks with any communication level at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized). To formalize our setting, we define a new class of multi-agent partially observable Markov decision processes (POMDPs) that we name hybrid-POMDPs, which explicitly models a communication process between the agents. We contribute MARO, an approach that combines an autoregressive predictive model to estimate missing agents' observations, and a dropout-based RL training scheme that simulates different communication levels during the centralized training phase. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the negative impact of partial observability in MARL. Experimental results show that our method consistently outperforms baselines, allowing agents to act with faulty communication while successfully exploiting shared information.