pwc-archive/papers-with-abstracts huggingface dataset:
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK
Authors: Runzhong Wang, Li Shen, Yiting Chen, Junchi Yan, Xiaokang Yang, DaCheng Tao
Date: 2021-09-29 00:00:00
Tasks: Combinatorial Optimization, One-Shot Learning, Self-Supervised Learning

Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network. However, the existing differentiable TopK operator suffers from an unbounded gap between the soft prediction and the discrete solution, leading to inaccurate estimation of the combinatorial objective score. In this paper, we present a self-supervised learning pipeline for cardinality constrained combinatorial optimization, which incorporates with Gumbel-Sinkhorn-TopK (GS-TopK) for near-discrete TopK predictions and the re-parameterization trick resolving the non-differentiable challenge. Theoretically, we characterize a bounded gap between the Maximum-A-Posteriori (MAP) inference and our proposed method, resolving the divergence issue in the previous differentiable TopK operator and also providing a more accurate estimation of the objective score given a provable tightened bound to the discrete decision variables. Experiments on max covering and discrete clustering problems show that our method outperforms state-of-the-art Gurobi solver and the novel one-shot learning method Erdos Goes Neural.
Diverse and Consistent Multi-view Networks for Semi-supervised Regression
Authors: Cuong Manh Nguyen, Le Zhang, Arun Raja, Xun Xu, Balagopal Unnikrishnan, Kangkang Lu, Chuan-Sheng Foo
Date: 2021-09-29 00:00:00
Tasks: Diversity, MULTI-VIEW LEARNING, regression

Label collection is costly in many applications, which poses the need for label-efficient learning. In this work, we present Diverse and Consistent Multi-view Networks (DiCoM) — a novel semi-supervised regression technique based on a multi-view learning framework. DiCoM combines diversity with consistency — two seemingly opposing yet complementary principles of multi-view learning - based on underlying probabilistic graphical assumptions. Given multiple deep views of the same input, DiCoM encourages a negative correlation among the views' predictions on labeled data, while simultaneously enforces their agreement on unlabeled data. DiCoM can utilize either multi-network or multi-branch architectures to make a trade-off between computational cost and modeling performance. Under realistic evaluation setups, DiCoM outperforms competing methods on tabular and image data. Our ablation studies confirm the importance of having both consistency and diversity.
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm
Authors: Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong
Date: 2021-09-29 00:00:00
Tasks: Atari Games, Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we interpret distributional RL as entropy-regularized maximum likelihood estimation in the \textit{neural Z-fitted iteration} framework and establish the connection of the resulting risk-aware regularization with maximum entropy RL. In addition, We shed light on the stability-promoting distributional loss with desirable smoothness properties in distributional RL, which can yield stable optimization and guaranteed generalization. We also analyze the acceleration behavior while optimizing distributional RL algorithms and show that an appropriate approximation to the true target distribution can speed up the convergence. From the perspective of representation, we find that distributional RL encourages state representation from the same action class classified by the policy in tighter clusters. Finally, we propose a class of \textit{Sinkhorn distributional RL} algorithm that interpolates between the Wasserstein distance and maximum mean discrepancy~(MMD). Experiments on a suite of Atari games reveal the competitive performance of our algorithm relative to existing state-of-the-art distributional RL algorithms.
Training with Worst-Case Distributional Shift causes Overestimation and Inaccuracies in State-Action Value Functions
Authors: Ezgi Korkmaz
Date: 2021-09-29 00:00:00
Tasks: Atari Games, Self-Learning

The utilization of deep neural networks as function approximators for the state-action value function created a new research area for self learning systems, and made it possible to learn optimal policies from high dimensional state representations. While this initial success led deep neural policies to be employed in many diverse disciplines with manifold applications, the issues related to their resilience with respect to specifically crafted imperceptible adversarial perturbations remains a concern. To eliminate these concerns several studies have focused on building deep neural policies resilient towards these perturbations via training with the presence of such perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We theoretically motivate that the idea behind the state-of-the-art adversarial training method causes overestimation bias and inaccuracies in the state-action value function. We perform several experiments in the Arcade Learning Environment (ALE) and show that indeed adversarially trained deep neural policies suffer from overestimation bias. Furthermore, the state-action value functions learned by vanilla trained deep neural policies have more accurate estimates for the non-optimal actions than state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be a critical step towards obtaining robust and reliable policies.
Learned Simulators for Turbulence
Authors: Kim Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter Battaglia, Alvaro Sanchez-Gonzalez
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Turbulence simulation with classical numerical solvers requires very high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics that were generated at high resolution. We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics. Our model is trained end-to-end from data and it is capable of learning a range of challenging chaotic and turbulent dynamics at low resolution, including trajectories generated by the state-of-the-art Athena++ engine. We show that our simpler, general-purpose architecture outperforms various more specialized, turbulence-specific architectures from the learned turbulence simulation literature. In general, we see that learned simulators can yield unstable trajectories; however, we show that tuning training noise and temporal downsampling solves this problem. We also find that while generalization beyond the training distribution is a challenge for learned models, training noise, convolutional architectures, and added loss constraints can help. Broadly, we conclude that our learned simulator outperforms traditional solvers run on coarser grids, and emphasize that simple design choices can offer stability and robust generalization.
BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps
Authors: Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang
Date: 2021-09-29 00:00:00

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components. Furthermore, most previous BNNs use 32-bit values for feature maps and residual shortcuts, which helps to maintain the accuracy, but is not friendly to hardware accelerators with limited memory, energy, and computing resources. Thus, we raise the following question: How can accuracy and energy consumption be balanced in a BNN design? We extensively study this fundamental problem in this work and propose BoolNet: an architecture without most commonly used 32-bit components that uses 1-bit values to store feature maps. Experimental results on ImageNet demonstrate that BoolNet can achieve 63.0% Top-1 accuracy coupled with an energy reduction of 2.95x compared to recent state-of-the-art BNN architectures. Code and trained models are available at: (URL in the final version).
Imperceptible Black-box Attack via Refining in Salient Region
Authors: Zeyu Dai, Shengcai Liu, Ke Tang, Qing Li
Date: 2021-09-29 00:00:00

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker only has query access to the model output. Recent studies have devised successful black-box attacks with high query efficiency. However, such performance often comes at the cost of the imperceptibility of adversarial attacks, which is essential for attackers. To address this issue, in this paper we propose to use segmentation priors for black-box attacks such that the perturbations are limited in the salient region. We find that state-of-the-art black-box attacks equipped with segmentation priors can achieve much better imperceptibility performance with little reduction in query efficiency and success rate. We further propose the Saliency Attack, a new gradient-free black-box attack that can further improve the imperceptibility by refining perturbations in the salient region. Experimental results show that the perturbations generated by our approach are much more imperceptible than the ones generated by other attacks, and are interpretable to some extent. Furthermore, our approach is found to be more robust to detection-based defense, which demonstrates its efficacy as well.
Message Function Search for Hyper-relational Knowledge Graph
Authors: Shimin Di, Lei Chen
Date: 2021-09-29 00:00:00
Tasks: Relation Prediction

Recently, the hyper-relational knowledge graph (HKG) has attracted much attention due to its widespread existence and potential applications. The pioneer works have adapted powerful graph neural networks (GNNs) to embed HKGs by proposing domain-specific message functions. These message functions for HKG embedding are utilized to learn relational representations and capture the correlation between entities and relations of HKGs. However, these works often manually design and fix structures and operators of message functions, which makes them difficult to handle complex and diverse relational patterns in various HKGs (i.e., data patterns). To overcome these shortcomings, we plan to develop a method to dynamically search suitable message functions that can adapt to patterns of the given HKG. Unfortunately, it is not trivial to design an expressive search space and an efficient search algorithm to make the search effective and efficient. In this paper, we first unify a search space of message functions that enables both structures and operators to be searchable. Especially, the classic KG/HKG models and message functions of existing GNNs can be instantiated as special cases in the proposed search space. Then, we design an efficient search algorithm to search the message function and other GNN components for any given HKGs. Through empirical study, we show that the searched message functions are data-dependent, and can achieve leading performance in link/relation prediction tasks on benchmark data sets.
CDNet: A cascaded decoupling architecture for video prediction
Authors: Chuanqi Zang, Mingtao Pei
Date: 2021-09-29 00:00:00
Tasks: Prediction, Video Prediction

Video prediction is an essential task in the computer vision community, helping to solve many downstream vision tasks by predicting and modeling future motion dynamics and appearance. In the deterministic video prediction task, current methods mainly employ variants of stacked Recurrent Neural Networks (RNN) to capture spatiotemporal coherence, overlooking the conflict between long-term motion dynamics modeling and legible appearance generation. In this work, we propose a Cascaded Decoupling Network (CDNet) to solve the video prediction problem through two modules: motion LSTM to capture the motion trend and variation in the temporal highway without considering the appearance details, and refine LSTM to recover the detailed appearance according to the predicted motion dynamics and historical appearance iteratively. The cascaded structure provides a preliminary solution for the above conflict. We verify the rationality of our model on two real-world challenging video prediction datasets and yield state-of-the-art performance.
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
Authors: Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Self-Supervised Learning

In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information. The views are generated by conducting series of data augmentation to the anchor samples. Although the data augmentation operations are often designed to be aggressive and extensive to lower the mutual information between views, the family of Information-Erasing data augmentation that masks out region of images is barely considered. In this work, we propose the Piecing and Chipping enhanced Erasing Augmentation (PCEA) approach to making the self-supervised learning algorithms benefit from the effectiveness of Information-Erasing data augmentation.  Specifically, we design a pipeline to generate mutually weakly related transformed views using random erasing and build corresponding loss terms to take advantage of these views. Extensive experiments demonstrate the effectiveness of our method. Particularly, applying our PCEA to MoCo v2 improves the baseline by 12.84\%, 3.3\% in terms of linear classification on ImageNet-100 and ImageNet-1K.
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
Authors: Joonhyung Park, Jaeyun Song, Eunho Yang
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Blocking, Classification, Node Classification

In many real-world node classification scenarios, nodes are highly class-imbalanced, where graph neural networks (GNNs) can be readily biased to major class instances. Albeit existing class imbalance approaches in other domains can alleviate this issue to some extent, they do not consider the impact of message passing between nodes. In this paper, we hypothesize that overfitting to the neighbor sets of minor class due to message passing is a major challenge for class-imbalanced node classification. To tackle this issue, we propose GraphENS, a novel augmentation method that synthesizes the whole ego network for minor class (minor node and its one-hop neighbors) by combining two different ego networks based on their similarity. Additionally, we introduce a saliency-based node mixing method to exploit the abundant class-generic attributes of other nodes while blocking the injection of class-specific features. Our approach consistently outperforms the baselines over multiple node classification benchmark datasets and architectures.
Interpreting Molecule Generative Models for Interactive Molecule Discovery
Authors: Yuanqi Du, Xian Liu, Shengchao Liu, Bolei Zhou
Date: 2021-09-29 00:00:00
Tasks: Drug Discovery

Discovering novel molecules with desired properties is crucial for advancing drug discovery and chemical science. Recently deep generative models can synthesize new molecules by sampling random vectors from latent space and then decoding them to a molecule structure. However, through the feedforward generation pipeline, it is difficult to reveal the underlying connections between latent space and molecular properties as well as customize the output molecule with desired properties. In this work, we develop a simple yet effective method to interpret the latent space of the learned generative models with various molecular properties for more interactive molecule generation and discovery. This method, called Molecular Space Explorer (MolSpacE), is model-agnostic and can work with any pre-trained molecule generative models in an off-the-shelf manner. It first identifies latent directions that govern certain molecular properties via the property separation hyperplane and then moves molecules along the directions for smooth change of molecular structures and properties. This method achieves interactive molecule discovery through identifying interpretable and steerable concepts that emerge in the representations of generative models. Experiments show that MolSpacE can manipulate the output molecule toward desired properties with high success. We further quantify and compare the interpretability of multiple state-of-the-art molecule generative models. An interface and a demo video are developed to illustrate the promising application of interactive molecule discovery.
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems
Authors: Hadi Abdullah, Aditya Karlekar, Vincent Bindschaedler, Patrick Traynor
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The targeted transferability of adversarial samples enables attackers to exploit black-box models in the real-world. The most popular method to produce these adversarial samples is optimization attacks, which have been shown to achieve a high level of transferability in some domains. However, recent research has demonstrated that these attack samples fail to transfer when applied to Automatic Speech Recognition Systems (ASRs). In this paper, we investigate factors preventing this transferability via exhaustive experimentation. To do so, we perform an ablation study on each stage of the ASR pipeline. We discover and quantify six factors (i.e., input type, MFCC, RNN, output type, and vocabulary and sequence sizes) that impact the targeted transferability of optimization attacks against ASRs. Future research can leverage our findings to build ASRs that are more robust to other transferable attack types (e.g., signal processing attacks), or to modify architectures in other domains to reduce their exposure to targeted transferability of optimization attacks.
DYNASHARE: DYNAMIC NEURAL NETWORKS FOR MULTI-TASK LEARNING
Authors: Golara Javadi, Frederick Tung, Gabriel L. Oliveira
Date: 2021-09-29 00:00:00
Tasks: Dynamic neural networks, Multi-Task Learning

Parameter sharing approaches for deep multi-task learning share a common intuition: for a single network to perform multiple prediction tasks, the network needs to support multiple specialized execution paths. However, previous parameter sharing approaches have relied on a static network structure for each task. In this paper, we propose to increase the capacity for a single network to support multiple tasks by radically increasing the space of possible specialized execution paths. DynaShare is a new approach to deep multi-task learning that learns from the training data a hierarchical gating policy consisting of a task-specific policy for coarse layer selection and gating units for individual input instances, which work together to determine the execution path at inference time. Experimental results on standard multi-task learning benchmark datasets demonstrate the potential of the proposed approach.
Learning Invariant Representations on Multilingual Language Models for Unsupervised Cross-Lingual Transfer
Authors: Ruicheng Xian, Heng Ji, Han Zhao
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Cross-Lingual Transfer, Domain Adaptation

Recent advances in neural modeling have produced deep multilingual language models capable of extracting cross-lingual knowledge from unparallel texts, as evidenced by their decent zero-shot transfer performance. While analyses have attributed this success to having cross-lingually shared representations, its contribution to transfer performance remains unquantified. Towards a better understanding, in this work, we first make the following observations through empirical analysis: (1) invariance of the feature representations strongly correlates with transfer performance, and (2) distributional shift in class priors between data in the source and target languages negatively affects performance---an issue that is largely overlooked in prior work. Based on our findings, we propose an unsupervised cross-lingual learning method, called importance-weighted domain adaptation (IWDA), that performs feature alignment, prior shift estimation, and correction. Experiment results demonstrate its superiority under large prior shifts. In addition, our method delivers further performance gains when combined with existing semi-supervised learning techniques.
Dissecting Local Properties of Adversarial Examples
Authors: Lu Chen, Renjie Chen, Hang Guo, Yuan Luo, Quanshi Zhang, Yisen Wang
Date: 2021-09-29 00:00:00
Tasks: Adversarial Robustness

Adversarial examples have attracted significant attention over the years, yet a sufficient understanding is in lack, especially when analyzing their performances in combination with adversarial training. In this paper, we revisit some properties of adversarial examples from both frequency and spatial perspectives: 1) the special high-frequency components of adversarial examples tend to mislead naturally-trained models while have little impact on adversarially-trained ones, and 2) adversarial examples show disorderly perturbations on naturally-trained models and locally-consistent (image shape related) perturbations on adversarially-trained ones.  Motivated by these, we analyze the fragile tendency of models with the generated adversarial perturbations, and propose a connection with model vulnerability and local intermediate response. That is, a smaller local intermediate response comes along with better model adversarial robustness. To be specific, we demonstrate that: 1) DNNs are naturally fragile at least for large enough local response differences between adversarial/natural examples, 2) and smoother adversarially-trained models can alleviate local response differences with enhanced robustness.