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Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits | https://openreview.net/forum?id=iKQAk8a2kM0 | https://openreview.net/forum?id=iKQAk8a2kM0 | Jiawang Bai,Baoyuan Wu,Yong Zhang,Yiming Li,Zhifeng Li,Shu-Tao Xia | ICLR 2021,Poster | To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Specifically, our goal is to misclassify a specific sample into a target class without any sample modification, while not significantly reduce the prediction accuracy of other samples to ensure the stealthiness. To this end, we formulate this problem as a binary integer programming (BIP), since the parameters are stored as binary bits ($i.e.$, 0 and 1) in the memory. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of our method in attacking DNNs. | https://openreview.net/pdf/ed4d75e28ae70ba28f4895cf7097cf634745d11a.pdf |
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | https://openreview.net/forum?id=6YEQUn0QICG | https://openreview.net/forum?id=6YEQUn0QICG | Xiaoxiao Li,Meirui JIANG,Xiaofei Zhang,Michael Kamp,Qi Dou | ICLR 2021,Poster | The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. Code is available at https://github.com/med-air/FedBN. | https://openreview.net/pdf/37e144ecec7a76ce5093d4ec328649e8ef208a5b.pdf |
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights | https://openreview.net/forum?id=Iz3zU3M316D | https://openreview.net/forum?id=Iz3zU3M316D | Byeongho Heo,Sanghyuk Chun,Seong Joon Oh,Dongyoon Han,Sangdoo Yun,Gyuwan Kim,Youngjung Uh,Jung-Woo Ha | ICLR 2021,Poster | Normalization techniques, such as batch normalization (BN), are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the normalization-induced scale invariance among the weights provides an advantageous ground for gradient descent (GD) optimizers: the effective step sizes are automatically reduced over time, stabilizing the overall training procedure. It is often overlooked, however, that the additional introduction of momentum in GD optimizers results in a far more rapid reduction in effective step sizes for scale-invariant weights, a phenomenon that has not yet been studied and may have caused unwanted side effects in the current practice. This is a crucial issue because arguably the vast majority of modern deep neural networks consist of (1) momentum-based GD (e.g. SGD or Adam) and (2) scale-invariant parameters (e.g. more than 90% of the weights in ResNet are scale-invariant due to BN). In this paper, we verify that the widely-adopted combination of the two ingredients lead to the premature decay of effective step sizes and sub-optimal model performances. We propose a simple and effective remedy, SGDP and AdamP: get rid of the radial component, or the norm-increasing direction, at each optimizer step. Because of the scale invariance, this modification only alters the effective step sizes without changing the effective update directions, thus enjoying the original convergence properties of GD optimizers. Given the ubiquity of momentum GD and scale invariance in machine learning, we have evaluated our methods against the baselines on 13 benchmarks. They range from vision tasks like classification (e.g. ImageNet), retrieval (e.g. CUB and SOP), and detection (e.g. COCO) to language modelling (e.g. WikiText) and audio classification (e.g. DCASE) tasks. We verify that our solution brings about uniform gains in performances in those benchmarks. Source code is available at https://github.com/clovaai/adamp | https://openreview.net/pdf/b3d064c86ebe3e60b1df68fff70ee335af15d5af.pdf |
Learning Subgoal Representations with Slow Dynamics | https://openreview.net/forum?id=wxRwhSdORKG | https://openreview.net/forum?id=wxRwhSdORKG | Siyuan Li,Lulu Zheng,Jianhao Wang,Chongjie Zhang | ICLR 2021,Poster | In goal-conditioned Hierarchical Reinforcement Learning (HRL), a high-level policy periodically sets subgoals for a low-level policy, and the low-level policy is trained to reach those subgoals. A proper subgoal representation function, which abstracts a state space to a latent subgoal space, is crucial for effective goal-conditioned HRL, since different low-level behaviors are induced by reaching subgoals in the compressed representation space. Observing that the high-level agent operates at an abstract temporal scale, we propose a slowness objective to effectively learn the subgoal representation (i.e., the high-level action space). We provide a theoretical grounding for the slowness objective. That is, selecting slow features as the subgoal space can achieve efficient hierarchical exploration. As a result of better exploration ability, our approach significantly outperforms state-of-the-art HRL and exploration methods on a number of benchmark continuous-control tasks. Thanks to the generality of the proposed subgoal representation learning method, empirical results also demonstrate that the learned representation and corresponding low-level policies can be transferred between distinct tasks. | https://openreview.net/pdf/72f082bc7ce485bf0a24a86ed48e9d0b9f0fb493.pdf |
A Unified Approach to Interpreting and Boosting Adversarial Transferability | https://openreview.net/forum?id=X76iqnUbBjz | https://openreview.net/forum?id=X76iqnUbBjz | Xin Wang,Jie Ren,Shuyun Lin,Xiangming Zhu,Yisen Wang,Quanshi Zhang | ICLR 2021,Poster | In this paper, we use the interaction inside adversarial perturbations to explain and boost the adversarial transferability. We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations. The negative correlation is further verified through different DNNs with various inputs. Moreover, this negative correlation can be regarded as a unified perspective to understand current transferability-boosting methods. To this end, we prove that some classic methods of enhancing the transferability essentially decease interactions inside adversarial perturbations. Based on this, we propose to directly penalize interactions during the attacking process, which significantly improves the adversarial transferability. We will release the code when the paper is accepted. | https://openreview.net/pdf/92ba2b486ce85a52e017691c5f53af8699796288.pdf |
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models | https://openreview.net/forum?id=dFwBosAcJkN | https://openreview.net/forum?id=dFwBosAcJkN | Cassidy Laidlaw,Sahil Singla,Soheil Feizi | ICLR 2021,Poster | A key challenge in adversarial robustness is the lack of a precise mathematical characterization of human perception, used in the definition of adversarial attacks that are imperceptible to human eyes. Most current attacks and defenses try to get around this issue by considering restrictive adversarial threat models such as those bounded by $L_2$ or $L_\infty$ distance, spatial perturbations, etc. However, models that are robust against any of these restrictive threat models are still fragile against other threat models, i.e. they have poor generalization to unforeseen attacks. Moreover, even if a model is robust against the union of several restrictive threat models, it is still susceptible to other imperceptible adversarial examples that are not contained in any of the constituent threat models. To resolve these issues, we propose adversarial training against the set of all imperceptible adversarial examples. Since this set is intractable to compute without a human in the loop, we approximate it using deep neural networks. We call this threat model the neural perceptual threat model (NPTM); it includes adversarial examples with a bounded neural perceptual distance (a neural network-based approximation of the true perceptual distance) to natural images. Through an extensive perceptual study, we show that the neural perceptual distance correlates well with human judgements of perceptibility of adversarial examples, validating our threat model.
Under the NPTM, we develop novel perceptual adversarial attacks and defenses. Because the NPTM is very broad, we find that Perceptual Adversarial Training (PAT) against a perceptual attack gives robustness against many other types of adversarial attacks. We test PAT on CIFAR-10 and ImageNet-100 against five diverse adversarial attacks: $L_2$, $L_\infty$, spatial, recoloring, and JPEG. We find that PAT achieves state-of-the-art robustness against the union of these five attacks—more than doubling the accuracy over the next best model—without training against any of them. That is, PAT generalizes well to unforeseen perturbation types. This is vital in sensitive applications where a particular threat model cannot be assumed, and to the best of our knowledge, PAT is the first adversarial training defense with this property.
Code and data are available at https://github.com/cassidylaidlaw/perceptual-advex | https://openreview.net/pdf/eb768a2d394baa94aebe2efba341cc5a5244428d.pdf |
Better Fine-Tuning by Reducing Representational Collapse | https://openreview.net/forum?id=OQ08SN70M1V | https://openreview.net/forum?id=OQ08SN70M1V | Armen Aghajanyan,Akshat Shrivastava,Anchit Gupta,Naman Goyal,Luke Zettlemoyer,Sonal Gupta | ICLR 2021,Poster | Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance. We also introduce a new analysis to motivate the use of trust region methods more generally, by studying representational collapse; the degradation of generalizable representations from pre-trained models as they are fine-tuned for a specific end task. Extensive experiments show that our fine-tuning method matches or exceeds the performance of previous trust region methods on a range of understanding and generation tasks (including DailyMail/CNN, Gigaword, Reddit TIFU, and the GLUE benchmark), while also being much faster. We also show that it is less prone to representation collapse; the pre-trained models maintain more generalizable representations every time they are fine-tuned. | https://openreview.net/pdf/2e098788f4b0c69470d410cbd519d3e0346848d3.pdf |
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch | https://openreview.net/forum?id=K9bw7vqp_s | https://openreview.net/forum?id=K9bw7vqp_s | Aojun Zhou,Yukun Ma,Junnan Zhu,Jianbo Liu,Zhijie Zhang,Kun Yuan,Wenxiu Sun,Hongsheng Li | ICLR 2021,Poster | Sparsity in Deep Neural Networks (DNNs) has been widely studied to compress and accelerate the models on resource-constrained environments. It can be generally categorized into unstructured fine-grained sparsity that zeroes out multiple individual weights distributed across the neural network, and structured coarse-grained sparsity which prunes blocks of sub-networks of a neural network. Fine-grained sparsity can achieve a high compression ratio but is not hardware friendly and hence receives limited speed gains. On the other hand, coarse-grained sparsity cannot simultaneously achieve both apparent acceleration on modern GPUs and
decent performance. In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs. Specifically, a 2 : 4 sparse network could achieve 2× speed-up without performance drop on Nvidia A100 GPUs. Furthermore, we propose a novel and effective ingredient, sparse-refined straight-through estimator (SR-STE), to alleviate the negative influence of the approximated gradients computed by vanilla STE during optimization. We also define a metric, Sparse Architecture Divergence (SAD), to measure the sparse network’s topology change during the training process. Finally, We justify SR-STE’s advantages with SAD and demonstrate the effectiveness of SR-STE by performing
comprehensive experiments on various tasks. Anonymous code and model will be at available at https://github.com/anonymous-NM-sparsity/NM-sparsity. | https://openreview.net/pdf/75cc29fc217b7a42a135a1f1ee7a57e605181177.pdf |
Active Contrastive Learning of Audio-Visual Video Representations | https://openreview.net/forum?id=OMizHuea_HB | https://openreview.net/forum?id=OMizHuea_HB | Shuang Ma,Zhaoyang Zeng,Daniel McDuff,Yale Song | ICLR 2021,Poster | Contrastive learning has been shown to produce generalizable representations of audio and visual data by maximizing the lower bound on the mutual information (MI) between different views of an instance. However, obtaining a tight lower bound requires a sample size exponential in MI and thus a large set of negative samples. We can incorporate more samples by building a large queue-based dictionary, but there are theoretical limits to performance improvements even with a large number of negative samples. We hypothesize that random negative sampling leads to a highly redundant dictionary that results in suboptimal representations for downstream tasks. In this paper, we propose an active contrastive learning approach that builds an actively sampled dictionary with diverse and informative items, which improves the quality of negative samples and improves performances on tasks where there is high mutual information in the data, e.g., video classification. Our model achieves state-of-the-art performance on challenging audio and visual downstream benchmarks including UCF101, HMDB51 and ESC50. | https://openreview.net/pdf/a696a70ba651de1c50d0a72fea6dc1c20d8bb37a.pdf |
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation | https://openreview.net/forum?id=-TwO99rbVRu | https://openreview.net/forum?id=-TwO99rbVRu | Yuliang Zou,Zizhao Zhang,Han Zhang,Chun-Liang Li,Xiao Bian,Jia-Bin Huang,Tomas Pfister | ICLR 2021,Poster | Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code will be released. | https://openreview.net/pdf/0ed366ea64452ea749b2aea97df25aa09830f5e1.pdf |
Counterfactual Generative Networks | https://openreview.net/forum?id=BXewfAYMmJw | https://openreview.net/forum?id=BXewfAYMmJw | Axel Sauer,Andreas Geiger | ICLR 2021,Poster | Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task. In this work, we take a step towards more robust and interpretable classifiers that explicitly expose the task's causal structure. Building on current advances in deep generative modeling, we propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background; hence, they allow for generating counterfactual images. We demonstrate the ability of our model to generate such images on MNIST and ImageNet. Further, we show that the counterfactual images can improve out-of-distribution robustness with a marginal drop in performance on the original classification task, despite being synthetic. Lastly, our generative model can be trained efficiently on a single GPU, exploiting common pre-trained models as inductive biases. | https://openreview.net/pdf/ef0dd5122ec4977085ea0ac017f21a3cb33377c5.pdf |
Contemplating Real-World Object Classification | https://openreview.net/forum?id=Q4EUywJIkqr | https://openreview.net/forum?id=Q4EUywJIkqr | ali borji | ICLR 2021,Poster | Deep object recognition models have been very successful over benchmark
datasets such as ImageNet. How accurate and robust are they to distribution
shifts arising from natural and synthetic variations in datasets? Prior research on
this problem has primarily focused on ImageNet variations (e.g., ImageNetV2,
ImageNet-A). To avoid potential inherited biases in these studies, we take a
different approach. Specifically, we reanalyze the ObjectNet dataset recently
proposed by Barbu et al. containing objects in daily life situations. They showed
a dramatic performance drop of the state of the art object recognition models on
this dataset. Due to the importance and implications of their results regarding
the generalization ability of deep models, we take a second look at their analysis.
We find that applying deep models to the isolated objects, rather than the entire
scene as is done in the original paper, results in around 20-30% performance
improvement. Relative to the numbers reported in Barbu et al., around 10-15%
of the performance loss is recovered, without any test time data augmentation.
Despite this gain, however, we conclude that deep models still suffer drastically
on the ObjectNet dataset. We also investigate the robustness of models against
synthetic image perturbations such as geometric transformations (e.g., scale,
rotation, translation), natural image distortions (e.g., impulse noise, blur) as well
as adversarial attacks (e.g., FGSM and PGD-5). Our results indicate that limiting
the object area as much as possible (i.e., from the entire image to the bounding
box to the segmentation mask) leads to consistent improvement in accuracy and
robustness. Finally, through a qualitative analysis of ObjectNet data, we find that
i) a large number of images in this dataset are hard to recognize even for humans,
and ii) easy (hard) samples for models match with easy (hard) samples for humans.
Overall, our analysis shows that ObjecNet is still a challenging test platform that
can be used to measure the generalization ability of models. The code and data
are available in [masked due to blind review]. | https://openreview.net/pdf/da3b5f5536c36c813eb1a7429c2e8b83585bc6d9.pdf |
Explainable Deep One-Class Classification | https://openreview.net/forum?id=A5VV3UyIQz | https://openreview.net/forum?id=A5VV3UyIQz | Philipp Liznerski,Lukas Ruff,Robert A. Vandermeulen,Billy Joe Franks,Marius Kloft,Klaus Robert Muller | ICLR 2021,Poster | Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a new state of the art in the unsupervised setting. Our method can incorporate ground-truth anomaly maps during training and using even a few of these (~5) improves performance significantly. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks. | https://openreview.net/pdf/faaddd67b70d461702cda54ff0c68b47fbf56d03.pdf |
Batch Reinforcement Learning Through Continuation Method | https://openreview.net/forum?id=po-DLlBuAuz | https://openreview.net/forum?id=po-DLlBuAuz | Yijie Guo,Shengyu Feng,Nicolas Le Roux,Ed Chi,Honglak Lee,Minmin Chen | ICLR 2021,Poster | Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions. Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efficiently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation. By constraining the difference between the learned policy and the behavior policy that generates the fixed trajectories, and continuously relaxing the constraint, our method 1) helps the agent escape local optima; 2) reduces the error in policy evaluation in the optimization procedure. We present results on a variety of control tasks, game environments, and a recommendation task to empirically demonstrate the efficacy of our proposed method. | https://openreview.net/pdf/84a7a35d996f84ab9fbbbcabccbdc21f44f2ba68.pdf |
Protecting DNNs from Theft using an Ensemble of Diverse Models | https://openreview.net/forum?id=LucJxySuJcE | https://openreview.net/forum?id=LucJxySuJcE | Sanjay Kariyappa,Atul Prakash,Moinuddin K Qureshi | ICLR 2021,Poster | Several recent works have demonstrated highly effective model stealing (MS) attacks on Deep Neural Networks (DNNs) in black-box settings, even when the training data is unavailable. These attacks typically use some form of Out of Distribution (OOD) data to query the target model and use the predictions obtained to train a clone model. Such a clone model learns to approximate the decision boundary of the target model, achieving high accuracy on in-distribution examples. We propose Ensemble of Diverse Models (EDM) to defend against such MS attacks. EDM is made up of models that are trained to produce dissimilar predictions for OOD inputs. By using a different member of the ensemble to service different queries, our defense produces predictions that are highly discontinuous in the input space for the adversary's OOD queries. Such discontinuities cause the clone model trained on these predictions to have poor generalization on in-distribution examples. Our evaluations on several image classification tasks demonstrate that EDM defense can severely degrade the accuracy of clone models (up to $39.7\%$). Our defense has minimal impact on the target accuracy, negligible computational costs during inference, and is compatible with existing defenses for MS attacks. | https://openreview.net/pdf/fb157da741cd6b15681066ac173712bdecd45316.pdf |
Domain Generalization with MixStyle | https://openreview.net/forum?id=6xHJ37MVxxp | https://openreview.net/forum?id=6xHJ37MVxxp | Kaiyang Zhou,Yongxin Yang,Yu Qiao,Tao Xiang | ICLR 2021,Poster | Though convolutional neural networks (CNNs) have demonstrated remarkable ability in learning discriminative features, they often generalize poorly to unseen domains. Domain generalization aims to address this problem by learning from a set of source domains a model that is generalizable to any unseen domain. In this paper, a novel approach is proposed based on probabilistically mixing instance-level feature statistics of training samples across source domains. Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e.g., photo vs.~sketch images). Such style information is captured by the bottom layers of a CNN where our proposed style-mixing takes place. Mixing styles of training instances results in novel domains being synthesized implicitly, which increase the domain diversity of the source domains, and hence the generalizability of the trained model. MixStyle fits into mini-batch training perfectly and is extremely easy to implement. The effectiveness of MixStyle is demonstrated on a wide range of tasks including category classification, instance retrieval and reinforcement learning. | https://openreview.net/pdf/e3135e8b22333ad40de9d8f855c3fb4253a2090b.pdf |
Efficient Inference of Flexible Interaction in Spiking-neuron Networks | https://openreview.net/forum?id=aGfU_xziEX8 | https://openreview.net/forum?id=aGfU_xziEX8 | Feng Zhou,Yixuan Zhang,Jun Zhu | ICLR 2021,Poster | Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities. Although utilized in many real applications, the classic Hawkes process is incapable of modelling inhibitory interactions among neurons. Instead, the nonlinear Hawkes process allows for a more flexible influence pattern with excitatory or inhibitory interactions. In this paper, three sets of auxiliary latent variables (Polya-Gamma variables, latent marked Poisson processes and sparsity variables) are augmented to make functional connection weights in a Gaussian form, which allows for a simple iterative algorithm with analytical updates. As a result, an efficient expectation-maximization (EM) algorithm is derived to obtain the maximum a posteriori (MAP) estimate. We demonstrate the accuracy and efficiency performance of our algorithm on synthetic and real data. For real neural recordings, we show our algorithm can estimate the temporal dynamics of interaction and reveal the interpretable functional connectivity underlying neural spike trains. | https://openreview.net/pdf/e38ae418aa78989951e0c37ca6c629aa4c6e96b7.pdf |
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation | https://openreview.net/forum?id=R2ZlTVPx0Gk | https://openreview.net/forum?id=R2ZlTVPx0Gk | Alexandre Rame,Matthieu Cord | ICLR 2021,Poster | Deep ensembles perform better than a single network thanks to the diversity among their members. Recent approaches regularize predictions to increase diversity; however, they also drastically decrease individual members’ performances. In this paper, we argue that learning strategies for deep ensembles need to tackle the trade-off between ensemble diversity and individual accuracies. Motivated by arguments from information theory and leveraging recent advances in neural estimation of conditional mutual information, we introduce a novel training criterion called DICE: it increases diversity by reducing spurious correlations among features. The main idea is that features extracted from pairs of members should only share information useful for target class prediction without being conditionally redundant. Therefore, besides the classification loss with information bottleneck, we adversarially prevent features from being conditionally predictable from each other. We manage to reduce simultaneous errors while protecting class information. We obtain state-of-the-art accuracy results on CIFAR-10/100: for example, an ensemble of 5 networks trained with DICE matches an ensemble of 7 networks trained independently. We further analyze the consequences on calibration, uncertainty estimation, out-of-distribution detection and online co-distillation. | https://openreview.net/pdf/d1c240af3d07df28ebfbd88feefb18b1e55be44e.pdf |
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning | https://openreview.net/forum?id=N33d7wjgzde | https://openreview.net/forum?id=N33d7wjgzde | Tsung-Wei Ke,Jyh-Jing Hwang,Stella Yu | ICLR 2021,Poster | Weakly supervised segmentation requires assigning a label to every pixel based on training instances with partial annotations such as image-level tags, object bounding boxes, labeled points and scribbles. This task is challenging, as coarse annotations (tags, boxes) lack precise pixel localization whereas sparse annotations (points, scribbles) lack broad region coverage. Existing methods tackle these two types of weak supervision differently: Class activation maps are used to localize coarse labels and iteratively refine the segmentation model, whereas conditional random fields are used to propagate sparse labels to the entire image.
We formulate weakly supervised segmentation as a semi-supervised metric learning problem, where pixels of the same (different) semantics need to be mapped to the same (distinctive) features. We propose 4 types of contrastive relationships between pixels and segments in the feature space, capturing low-level image similarity, semantic annotation, co-occurrence, and feature affinity They act as priors; the pixel-wise feature can be learned from training images with any partial annotations in a data-driven fashion. In particular, unlabeled pixels in training images participate not only in data-driven grouping within each image, but also in discriminative feature learning within and across images. We deliver a universal weakly supervised segmenter with significant gains on Pascal VOC and DensePose. Our code is publicly available at https://github.com/twke18/SPML. | https://openreview.net/pdf/4e7e3f71b7b4a72b0db3a1576e17b7acb8f9e435.pdf |
Shape-Texture Debiased Neural Network Training | https://openreview.net/forum?id=Db4yerZTYkz | https://openreview.net/forum?id=Db4yerZTYkz | Yingwei Li,Qihang Yu,Mingxing Tan,Jieru Mei,Peng Tang,Wei Shen,Alan Yuille,cihang xie | ICLR 2021,Poster | Shape and texture are two prominent and complementary cues for recognizing objects. Nonetheless, Convolutional Neural Networks are often biased towards either texture or shape, depending on the training dataset. Our ablation shows that such bias degenerates model performance. Motivated by this observation, we develop a simple algorithm for shape-texture debiased learning. To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.
Experiments show that our method successfully improves model performance on several image recognition benchmarks and adversarial robustness. For example, by training on ImageNet, it helps ResNet-152 achieve substantial improvements on ImageNet (+1.2%), ImageNet-A (+5.2%), ImageNet-C (+8.3%) and Stylized-ImageNet (+11.1%), and on defending against FGSM adversarial attacker on ImageNet (+14.4%). Our method also claims to be compatible with other advanced data augmentation strategies, eg, Mixup, and CutMix. The code is available here: https://github.com/LiYingwei/ShapeTextureDebiasedTraining. | https://openreview.net/pdf/95feebd9ddd0cc554ef18bf3b5cfce3f76bb9dd6.pdf |
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling | https://openreview.net/forum?id=IDFQI9OY6K | https://openreview.net/forum?id=IDFQI9OY6K | Benedikt Boecking,Willie Neiswanger,Eric Xing,Artur Dubrawski | ICLR 2021,Poster | Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large datasets and has demonstrated state of the art performance in diverse domains such as healthcare and e-commerce. One practical issue with learning from user-generated heuristics is that their creation requires creativity, foresight, and domain expertise from those who hand-craft them, a process which can be tedious and subjective. We develop the first framework for interactive weak supervision in which a method proposes heuristics and learns from user feedback given on each proposed heuristic. Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels. We conduct user studies, which show that users are able to effectively provide feedback on heuristics and that test set results track the performance of simulated oracles. | https://openreview.net/pdf/7b8b0c300266ed08c27bfea5eaaa513189d0caab.pdf |
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond | https://openreview.net/forum?id=8e6BrwU6AjQ | https://openreview.net/forum?id=8e6BrwU6AjQ | Duy Kien Nguyen,Vedanuj Goswami,Xinlei Chen | ICLR 2021,Poster | This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category). Unlike most prior works that use explicit, symbolic models which can be computationally expensive and limited in generalization, we propose a simple and effective alternative by revisiting modulated convolutions that fuse the query and the image locally. Following the design of residual bottleneck, we call our method MoVie, short for Modulated conVolutional bottlenecks. Notably, MoVie reasons implicitly and holistically and only needs a single forward-pass during inference. Nevertheless, MoVie showcases strong performance for counting: 1) advancing the state-of-the-art on counting-specific VQA tasks while being more efficient; 2) outperforming prior-art on difficult benchmarks like COCO for common object counting; 3) helped us secure the first place of 2020 VQA challenge when integrated as a module for ‘number’ related questions in generic VQA models. Finally, we show evidence that modulated convolutions such as MoVie can serve as a general mechanism for reasoning tasks beyond counting. | https://openreview.net/pdf/9b2e703f15aaf726a40dd681114694fe508bdeaf.pdf |
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient Detectors | https://openreview.net/forum?id=uKhGRvM8QNH | https://openreview.net/forum?id=uKhGRvM8QNH | Linfeng Zhang,Kaisheng Ma | ICLR 2021,Poster | Knowledge distillation, in which a student model is trained to mimic a teacher model, has been proved as an effective technique for model compression and model accuracy boosting. However, most knowledge distillation methods, designed for image classification, have failed on more challenging tasks, such as object detection. In this paper, we suggest that the failure of knowledge distillation on object detection is mainly caused by two reasons: (1) the imbalance between pixels of foreground and background and (2) lack of distillation on the relation between different pixels. Observing the above reasons, we propose attention-guided distillation and non-local distillation to address the two problems, respectively. Attention-guided distillation is proposed to find the crucial pixels of foreground objects with attention mechanism and then make the students take more effort to learn their features. Non-local distillation is proposed to enable students to learn not only the feature of an individual pixel but also the relation between different pixels captured by non-local modules. Experiments show that our methods achieve excellent AP improvements on both one-stage and two-stage, both anchor-based and anchor-free detectors. For example, Faster RCNN (ResNet101 backbone) with our distillation achieves 43.9 AP on COCO2017, which is 4.1 higher than the baseline. Codes have been released on Github. | https://openreview.net/pdf/1e6969024e2fab8681c02ff62a2dbfc4feedcff4.pdf |
Revisiting Dynamic Convolution via Matrix Decomposition | https://openreview.net/forum?id=YwpZmcAehZ | https://openreview.net/forum?id=YwpZmcAehZ | Yunsheng Li,Yinpeng Chen,Xiyang Dai,mengchen liu,Dongdong Chen,Ye Yu,Lu Yuan,Zicheng Liu,Mei Chen,Nuno Vasconcelos | ICLR 2021,Poster | Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels. It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging. In this paper, we revisit it from a new perspective of matrix decomposition and reveal the key issue is that dynamic convolution applies dynamic attention over channel groups after projecting into a higher dimensional latent space. To address this issue, we propose dynamic channel fusion to replace dynamic attention over channel groups. Dynamic channel fusion not only enables significant dimension reduction of the latent space, but also mitigates the joint optimization difficulty. As a result, our method is easier to train and requires significantly fewer parameters without sacrificing accuracy. Source code is at https://github.com/liyunsheng13/dcd. | https://openreview.net/pdf/e60d43801b1591f24c4abdca3a995b42eecd1fdf.pdf |
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation | https://openreview.net/forum?id=GTGb3M_KcUl | https://openreview.net/forum?id=GTGb3M_KcUl | Minjia Zhang,Menghao Li,Chi Wang,Mingqin Li | ICLR 2021,Poster | Recently, the DL compiler, together with Learning to Compile has proven to be a powerful technique for optimizing deep learning models. However, existing methods focus on accelerating the convergence speed of the individual tensor operator rather than the convergence speed of the entire model, which results in long optimization time to obtain a desired latency.
In this paper, we present a new method called DynaTune, which provides significantly faster convergence speed to optimize a DNN model. In particular, we consider a Multi-Armed Bandit (MAB) model for the tensor program optimization problem. We use UCB to handle the decision-making of time-slot-based optimization, and we devise a Bayesian belief model that allows predicting the potential performance gain of each operator with uncertainty quantification, which guides the optimization process. We evaluate and compare DynaTune with the state-of-the-art DL compiler. The experiment results show that DynaTune is 1.2--2.4 times faster to achieve the same optimization quality for a range of models across different hardware architectures. | https://openreview.net/pdf/f2330b850544ed7b0157ff0411638fd7ee8aefc0.pdf |
MALI: A memory efficient and reverse accurate integrator for Neural ODEs | https://openreview.net/forum?id=blfSjHeFM_e | https://openreview.net/forum?id=blfSjHeFM_e | Juntang Zhuang,Nicha C Dvornek,sekhar tatikonda,James s Duncan | ICLR 2021,Poster | Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the naive method and the adaptive checkpoint adjoint method (ACA) have a memory cost that grows with integration time. In this project, based on the asynchronous leapfrog (ALF) solver, we propose the Memory-efficient ALF Integrator (MALI), which has a constant memory cost $w.r.t$ integration time similar to the adjoint method, and guarantees accuracy in reverse-time trajectory (hence accuracy in gradient estimation). We validate MALI in various tasks: on image recognition tasks, to our knowledge, MALI is the first to enable feasible training of a Neural ODE on ImageNet and outperform a well-tuned ResNet, while existing methods fail due to either heavy memory burden or inaccuracy; for time series modeling, MALI significantly outperforms the adjoint method; and for continuous generative models, MALI achieves new state-of-the-art performance. We provide a pypi package: https://jzkay12.github.io/TorchDiffEqPack | https://openreview.net/pdf/f29cac7ba7ac28fa5acb37e735fd0661765f005e.pdf |
Bag of Tricks for Adversarial Training | https://openreview.net/forum?id=Xb8xvrtB8Ce | https://openreview.net/forum?id=Xb8xvrtB8Ce | Tianyu Pang,Xiao Yang,Yinpeng Dong,Hang Su,Jun Zhu | ICLR 2021,Poster | Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are less effective than simply early stopping the training procedure. This counter-intuitive fact motivates us to investigate the implementation details of tens of AT methods. Surprisingly, we find that the basic settings (e.g., weight decay, training schedule, etc.) used in these methods are highly inconsistent. In this work, we provide comprehensive evaluations on CIFAR-10, focusing on the effects of mostly overlooked training tricks and hyperparameters for adversarially trained models. Our empirical observations suggest that adversarial robustness is much more sensitive to some basic training settings than we thought. For example, a slightly different value of weight decay can reduce the model robust accuracy by more than $7\%$, which is probable to override the potential promotion induced by the proposed methods. We conclude a baseline training setting and re-implement previous defenses to achieve new state-of-the-art results. These facts also appeal to more concerns on the overlooked confounders when benchmarking defenses. | https://openreview.net/pdf/cd34c578852869a39b386b91bdb51a8e91255111.pdf |
Learning to Generate 3D Shapes with Generative Cellular Automata | https://openreview.net/forum?id=rABUmU3ulQh | https://openreview.net/forum?id=rABUmU3ulQh | Dongsu Zhang,Changwoon Choi,Jeonghwan Kim,Young Min Kim | ICLR 2021,Poster | In this work, we present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes. We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution. The transition kernel employs the local update rules of cellular automata, effectively reducing the search space in a high-resolution 3D grid space by exploiting the connectivity and sparsity of 3D shapes. Our progressive generation only focuses on the sparse set of occupied voxels and their neighborhood, thus enables the utilization of an expressive sparse convolutional network. We propose an effective training scheme to obtain the local homogeneous rule of generative cellular automata with sequences that are slightly different from the sampling chain but converge to the full shapes in the training data. Extensive experiments on probabilistic shape completion and shape generation demonstrate that our method achieves competitive performance against recent methods. | https://openreview.net/pdf/859e40f96ad083b6715504dbb3c3340a92590b79.pdf |
SEED: Self-supervised Distillation For Visual Representation | https://openreview.net/forum?id=AHm3dbp7D1D | https://openreview.net/forum?id=AHm3dbp7D1D | Zhiyuan Fang,Jianfeng Wang,Lijuan Wang,Lei Zhang,Yezhou Yang,Zicheng Liu | ICLR 2021,Poster | This paper is concerned with self-supervised learning for small models. The problem is motivated by our empirical studies that while the widely used contrastive self-supervised learning method has shown great progress on large model training, it does not work well for small models. To address this problem, we propose a new learning paradigm, named $\textbf{SE}$lf-Sup$\textbf{E}$rvised $\textbf{D}$istillation (${\large S}$EED), where we leverage a larger network (as Teacher) to transfer its representational knowledge into a smaller architecture (as Student) in a self-supervised fashion. Instead of directly learning from unlabeled data, we train a student encoder to mimic the similarity score distribution inferred by a teacher over a set of instances. We show that ${\large S}$EED dramatically boosts the performance of small networks on downstream tasks. Compared with self-supervised baselines, ${\large S}$EED improves the top-1 accuracy from 42.2% to 67.6% on EfficientNet-B0 and from 36.3% to 68.2% on MobileNet-v3-Large on the ImageNet-1k dataset. | https://openreview.net/pdf/1f85b790d971bca718badf148e1c89397c3c4d90.pdf |
PDE-Driven Spatiotemporal Disentanglement | https://openreview.net/forum?id=vLaHRtHvfFp | https://openreview.net/forum?id=vLaHRtHvfFp | Jérémie Donà,Jean-Yves Franceschi,sylvain lamprier,patrick gallinari | ICLR 2021,Poster | A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential equations: the separation of variables. This inspiration allows us to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a principled model based on learning disentangled spatial and temporal representations of a phenomenon to accurately predict future observations. We experimentally demonstrate the performance and broad applicability of our method against prior state-of-the-art models on physical and synthetic video datasets. | https://openreview.net/pdf/8fa879c43fc60a3ebca7377ceeb7bef505d19d0c.pdf |
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective | https://openreview.net/forum?id=gIHd-5X324 | https://openreview.net/forum?id=gIHd-5X324 | Helong Zhou,Liangchen Song,Jiajie Chen,Ye Zhou,Guoli Wang,Junsong Yuan,Qian Zhang | ICLR 2021,Poster | Knowledge distillation is an effective approach to leverage a well-trained network or an ensemble of them, named as the teacher, to guide the training of a student network. The outputs from the teacher network are used as soft labels for supervising the training of a new network. Recent studies (M ̈uller et al., 2019; Yuan et al., 2020) revealed an intriguing property of the soft labels that making labels soft serves as a good regularization to the student network. From the perspective of statistical learning, regularization aims to reduce the variance, however how bias and variance change is not clear for training with soft labels. In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels. Specifically, we observe that during training the bias-variance tradeoff varies sample-wisely. Further, under the same distillation temperature setting, we observe that the distillation performance is negatively associated with the number of some specific samples, which are named as regularization samples since these samples lead to bias increasing and variance decreasing. Nevertheless, we empirically find that completely filtering out regularization samples also deteriorates distillation performance. Our discoveries inspired us to propose the novel weighted soft labels to help the network adaptively handle the sample-wise bias-variance tradeoff. Experiments on standard evaluation benchmarks validate the effectiveness of our method. Our code is available in the supplementary. | https://openreview.net/pdf/2e59cd77d556e74ed337a3cd2829da3be79dc212.pdf |
Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent | https://openreview.net/forum?id=H8UHdhWG6A3 | https://openreview.net/forum?id=H8UHdhWG6A3 | El Mahdi El Mhamdi,Rachid Guerraoui,Sébastien Rouault | ICLR 2021,Poster | Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting.
Two recent attacks have been challenging state-of-the-art defenses though, often successfully precluding the model from even fitting the training set.
The main identified weakness in current defenses is their requirement of a sufficiently low variance-norm ratio for the stochastic gradients.
We propose a practical method which, despite increasing the variance, reduces the variance-norm ratio, mitigating the identified weakness.
We assess the effectiveness of our method over 736 different training configurations, comprising the 2 state-of-the-art attacks and 6 defenses.
For confidence and reproducibility purposes, each configuration is run 5 times with specified seeds (1 to 5), totalling 3680 runs.
In our experiments, when the attack is effective enough to decrease the highest observed top-1 cross-accuracy by at least 20% compared to the unattacked run, our technique systematically increases back the highest observed accuracy, and is able to recover at least 20% in more than 60% of the cases. | https://openreview.net/pdf/0bf4965da02ac45abfd26b1d5098ad915f6b89ff.pdf |
Scaling the Convex Barrier with Active Sets | https://openreview.net/forum?id=uQfOy7LrlTR | https://openreview.net/forum?id=uQfOy7LrlTR | Alessandro De Palma,Harkirat Behl,Rudy R Bunel,Philip Torr,M. Pawan Kumar | ICLR 2021,Poster | Tight and efficient neural network bounding is of critical importance for the scaling of neural network verification systems. A number of efficient specialised dual solvers for neural network bounds have been presented recently, but they are often too loose to verify more challenging properties. This lack of tightness is linked to the weakness of the employed relaxation, which is usually a linear program of size linear in the number of neurons. While a tighter linear relaxation for piecewise linear activations exists, it comes at the cost of exponentially many constraints and thus currently lacks an efficient customised solver. We alleviate this deficiency via a novel dual algorithm that realises the full potential of the new relaxation by operating on a small active set of dual variables. Our method recovers the strengths of the new relaxation in the dual space: tightness and a linear separation oracle. At the same time, it shares the benefits of previous dual approaches for weaker relaxations: massive parallelism, GPU implementation, low cost per iteration and valid bounds at any time. As a consequence, we obtain better bounds than off-the-shelf solvers in only a fraction of their running time and recover the speed-accuracy trade-offs of looser dual solvers if the computational budget is small. We demonstrate that this results in significant formal verification speed-ups. | https://openreview.net/pdf/2458e1a8af2e8df1e3da86a42fb7ebcbad62dc2f.pdf |
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization | https://openreview.net/forum?id=TiXl51SCNw8 | https://openreview.net/forum?id=TiXl51SCNw8 | Huanrui Yang,Lin Duan,Yiran Chen,Hai Li | ICLR 2021,Poster | Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated. However, it lacks a systematic method to determine the exact quantization scheme. Previous methods either examine only a small manually-designed search space or utilize a cumbersome neural architecture search to explore the vast search space. These approaches cannot lead to an optimal quantization scheme efficiently. This work proposes bit-level sparsity quantization (BSQ) to tackle the mixed-precision quantization from a new angle of inducing bit-level sparsity. We consider each bit of quantized weights as an independent trainable variable and introduce a differentiable bit-sparsity regularizer. BSQ can induce all-zero bits across a group of weight elements and realize the dynamic precision reduction, leading to a mixed-precision quantization scheme of the original model. Our method enables the exploration of the full mixed-precision space with a single gradient-based optimization process, with only one hyperparameter to tradeoff the performance and compression. BSQ achieves both higher accuracy and higher bit reduction on various model architectures on the CIFAR-10 and ImageNet datasets comparing to previous methods. | https://openreview.net/pdf/85f47585c645bbb6e9d4e2306480002404a37c22.pdf |
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling | https://openreview.net/forum?id=Pz_dcqfcKW8 | https://openreview.net/forum?id=Pz_dcqfcKW8 | Jiahui Yu,Wei Han,Anmol Gulati,Chung-Cheng Chiu,Bo Li,Tara N Sainath,Yonghui Wu,Ruoming Pang | ICLR 2021,Poster | Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses. In this work, we propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for both streaming and full-context speech recognition. We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation during the training. The Dual-mode ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks. We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and a large-scale dataset MultiDomain. Experiments and ablation studies demonstrate that Dual-mode ASR not only simplifies the workflow of training and deploying streaming and full-context ASR models, but also significantly improves both emission latency and recognition accuracy of streaming ASR. With Dual-mode ASR, we achieve new state-of-the-art streaming ASR results on both LibriSpeech and MultiDomain in terms of accuracy and latency. | https://openreview.net/pdf/8697733aaccbfee4496ccbbfa1b3ce6d706fd1a5.pdf |
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | https://openreview.net/forum?id=pzpytjk3Xb2 | https://openreview.net/forum?id=pzpytjk3Xb2 | Ziang Yan,Yiwen Guo,Jian Liang,Changshui Zhang | ICLR 2021,Poster | To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback. Existing black-box attacks generally suffer from high query complexity, especially when only the top-1 decision (i.e., the hard-label prediction) of the victim model is available. In this paper, we propose a novel hard-label black-box attack named Policy-Driven Attack, to reduce the query complexity. Our core idea is to learn promising search directions of the adversarial examples using a well-designed policy network in a novel reinforcement learning formulation, in which the queries become more sensible. Experimental results demonstrate that our method can significantly reduce the query complexity in comparison with existing state-of-the-art hard-label black-box attacks on various image classification benchmark datasets. Code and models for reproducing our results are available at https://github.com/ZiangYan/pda.pytorch | https://openreview.net/pdf/7095c4811250b0fb464739f87a3151931d53f718.pdf |
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences | https://openreview.net/forum?id=O3bqkf_Puys | https://openreview.net/forum?id=O3bqkf_Puys | Hehe Fan,Xin Yu,Yuhang Ding,Yi Yang,Mohan Kankanhalli | ICLR 2021,Poster | Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to spatio-temporal modeling of raw point cloud sequences. In this paper, we propose a point spatio-temporal (PST) convolution to achieve informative representations of point cloud sequences. The proposed PST convolution first disentangles space and time in point cloud sequences. Then, a spatial convolution is employed to capture the local structure of points in the 3D space, and a temporal convolution is used to model the dynamics of the spatial regions along the time dimension. Furthermore, we incorporate the proposed PST convolution into a deep network, namely PSTNet, to extract features of point cloud sequences in a hierarchical manner. Extensive experiments on widely-used 3D action recognition and 4D semantic segmentation datasets demonstrate the effectiveness of PSTNet to model point cloud sequences. | https://openreview.net/pdf/dd0f8643c1b3bba08e1c92689929511e4658374a.pdf |
Network Pruning That Matters: A Case Study on Retraining Variants | https://openreview.net/forum?id=Cb54AMqHQFP | https://openreview.net/forum?id=Cb54AMqHQFP | Duong Hoang Le,Binh-Son Hua | ICLR 2021,Poster | Network pruning is an effective method to reduce the computational expense of over-parameterized neural networks for deployment on low-resource systems. Recent state-of-the-art techniques for retraining pruned networks such as weight rewinding and learning rate rewinding have been shown to outperform the traditional fine-tuning technique in recovering the lost accuracy (Renda et al., 2020), but so far it is unclear what accounts for such performance. In this work, we conduct extensive experiments to verify and analyze the uncanny effectiveness of learning rate rewinding. We find that the reason behind the success of learning rate rewinding is the usage of a large learning rate. Similar phenomenon can be observed in other learning rate schedules that involve large learning rates, e.g., the 1-cycle learning rate schedule (Smith et al., 2019). By leveraging the right learning rate schedule in retraining, we demonstrate a counter-intuitive phenomenon in that randomly pruned networks could even achieve better performance than methodically pruned networks (fine-tuned with the conventional approach). Our results emphasize the cruciality of the learning rate schedule in pruned network retraining - a detail often overlooked by practitioners during the implementation of network pruning. | https://openreview.net/pdf/cd800d481759bd2472d081c050f8be1d94f91760.pdf |
EEC: Learning to Encode and Regenerate Images for Continual Learning | https://openreview.net/forum?id=lWaz5a9lcFU | https://openreview.net/forum?id=lWaz5a9lcFU | Ali Ayub,Alan Wagner | ICLR 2021,Poster | The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. Reconstructed images from encoded episodes are replayed when training the classifier model on a new task to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable with less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space. | https://openreview.net/pdf/e0fae0a996116aba06f3a1e0ff97fd0078ca47d2.pdf |
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators | https://openreview.net/forum?id=KLH36ELmwIB | https://openreview.net/forum?id=KLH36ELmwIB | Xiangxiang Chu,Xiaoxing Wang,Bo Zhang,Shun Lu,Xiaolin Wei,Junchi Yan | ICLR 2021,Poster | Despite the fast development of differentiable architecture search (DARTS), it suffers from a standing instability issue regarding searching performance, which extremely limits its application. Existing robustifying methods draw clues from the outcome instead of finding out the causing factor. Various indicators such as Hessian eigenvalues are proposed as a signal of performance collapse, and the searching should be stopped once an indicator reaches a preset threshold.
However, these methods tend to easily reject good architectures if thresholds are inappropriately set, let alone the searching is intrinsically noisy. In this paper, we undertake a more subtle and direct approach to resolve the collapse.
We first demonstrate that skip connections with a learnable architectural coefficient can easily recover from a disadvantageous state and become dominant. We conjecture that skip connections profit too much from this privilege, hence causing the collapse for the derived model. Therefore, we propose to factor out this benefit with an auxiliary skip connection, ensuring a fairer competition for all operations. Extensive experiments on various datasets verify that our approach can substantially improve the robustness of DARTS. Our code is available at https://github.com/Meituan-AutoML/DARTS- | https://openreview.net/pdf/7a1997d523c7fda09b7c32a482730abe9736ffa1.pdf |
BiPointNet: Binary Neural Network for Point Clouds | https://openreview.net/forum?id=9QLRCVysdlO | https://openreview.net/forum?id=9QLRCVysdlO | Haotong Qin,Zhongang Cai,Mingyuan Zhang,Yifu Ding,Haiyu Zhao,Shuai Yi,Xianglong Liu,Hao Su | ICLR 2021,Poster | To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds. We discover that the immense performance drop of binarized models for point clouds mainly stems from two challenges: aggregation-induced feature homogenization that leads to a degradation of information entropy, and scale distortion that hinders optimization and invalidates scale-sensitive structures. With theoretical justifications and in-depth analysis, our BiPointNet introduces Entropy-Maximizing Aggregation (EMA) to modulate the distribution before aggregation for the maximum information entropy, and Layer-wise Scale Recovery (LSR) to efficiently restore feature representation capacity. Extensive experiments show that BiPointNet outperforms existing binarization methods by convincing margins, at the level even comparable with the full precision counterpart. We highlight that our techniques are generic, guaranteeing significant improvements on various fundamental tasks and mainstream backbones. Moreover, BiPointNet gives an impressive 14.7× speedup and 18.9× storage saving on real-world resource-constrained devices. | https://openreview.net/pdf/62f2efb4dc751dae2aae03cda22f701192bfb771.pdf |
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition | https://openreview.net/forum?id=bM3L3I_853 | https://openreview.net/forum?id=bM3L3I_853 | Yue Meng,Rameswar Panda,Chung-Ching Lin,Prasanna Sattigeri,Leonid Karlinsky,Kate Saenko,Aude Oliva,Rogerio Feris | ICLR 2021,Poster | Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing temporal redundancy and reusing past features can significantly save computation leading to efficient action recognition. In this paper, we introduce an adaptive temporal fusion network, called AdaFuse, that dynamically fuses channels from current and past feature maps for strong temporal modelling. Specifically, the necessary information from the historical convolution feature maps is fused with current pruned feature maps with the goal of improving both recognition accuracy and efficiency. In addition, we use a skipping operation to further reduce the computation cost of action recognition. Extensive experiments on SomethingV1 & V2, Jester and Mini-Kinetics show that our approach can achieve about 40% computation savings with comparable accuracy to state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AdaFuse/ | https://openreview.net/pdf/9857965fe38df54f2da0ca74f21c7eedb09cdce7.pdf |
Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains | https://openreview.net/forum?id=M88oFvqp_9 | https://openreview.net/forum?id=M88oFvqp_9 | Utkarsh Ojha,Krishna Kumar Singh,Yong Jae Lee | ICLR 2021,Poster | We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distribution, which borrows a subset of properties from each domain, enabling the generation of images that did not exist in any domain exclusively. This challenging problem requires an accurate disentanglement of object shape, appearance, and background from each domain, so that the appearance and shape factors from the two domains can be interchanged. We augment an existing approach that can disentangle factors within a single domain but struggles to do so across domains. Our key technical contribution is to represent object appearance with a differentiable histogram of visual features, and to optimize the generator so that two images with the same latent appearance factor but different latent shape factors produce similar histograms. On multiple multi-domain datasets, we demonstrate our method leads to accurate and consistent appearance and shape transfer across domains. | https://openreview.net/pdf/b8b31818deaaa37925dbeb6143231cd0ef62b630.pdf |
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? | https://openreview.net/forum?id=Ig-VyQc-MLK | https://openreview.net/forum?id=Ig-VyQc-MLK | Jonathan Frankle,Gintare Karolina Dziugaite,Daniel Roy,Michael Carbin | ICLR 2021,Poster | Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why. We show that, unlike pruning after training, randomly shuffling the weights these methods prune within each layer or sampling new initial values preserves or improves accuracy. As such, the per-weight pruning decisions made by these methods can be replaced by a per-layer choice of the fraction of weights to prune. This property suggests broader challenges with the underlying pruning heuristics, the desire to prune at initialization, or both. | https://openreview.net/pdf/cca2ba851f4bc02f930894a6b4954aa465868ba6.pdf |
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction | https://openreview.net/forum?id=POWv6hDd9XH | https://openreview.net/forum?id=POWv6hDd9XH | Yuhang Li,Ruihao Gong,Xu Tan,Yang Yang,Peng Hu,Qi Zhang,Fengwei Yu,Wei Wang,Shi Gu | ICLR 2021,Poster | We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data but produces less powerful quantized models than Quantization-Aware Training (QAT). In this work, we propose a novel PTQ framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to INT2 for the first time. BRECQ leverages the basic building blocks in neural networks and reconstructs them one-by-one. In a comprehensive theoretical study of the second-order error, we show that BRECQ achieves a good balance between cross-layer dependency and generalization error. To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity. Extensive experiments on various handcrafted and searched neural architectures are conducted for both image classification and object detection tasks. And for the first time we prove that, without bells and whistles, PTQ can attain 4-bit ResNet and MobileNetV2 comparable with QAT and enjoy 240 times faster production of quantized models. Codes are available at https://github.com/yhhhli/BRECQ. | https://openreview.net/pdf/1d36f62d5b1b84de637026ab1c69e37fe5b20613.pdf |
Practical Real Time Recurrent Learning with a Sparse Approximation | https://openreview.net/forum?id=q3KSThy2GwB | https://openreview.net/forum?id=q3KSThy2GwB | Jacob Menick,Erich Elsen,Utku Evci,Simon Osindero,Karen Simonyan,Alex Graves | ICLR 2021,Spotlight | Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights "online" (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that are made highly sparse.
We introduce the Sparse n-step Approximation (SnAp) to the RTRL influence matrix. SnAp only tracks the influence of a parameter on hidden units that are reached by the computation graph within $n$ timesteps of the recurrent core. SnAp with $n=1$ is no more expensive than backpropagation but allows training on arbitrarily long sequences. We find that it substantially outperforms other RTRL approximations with comparable costs such as Unbiased Online Recurrent Optimization. For highly sparse networks, SnAp with $n=2$ remains tractable and can outperform backpropagation through time in terms of learning speed when updates are done online. | https://openreview.net/pdf/b366e30679c68aeab4a7151898074495dbf16d01.pdf |
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | https://openreview.net/forum?id=zv-typ1gPxA | https://openreview.net/forum?id=zv-typ1gPxA | Shangqing Liu,Yu Chen,Xiaofei Xie,Jing Kai Siow,Yang Liu | ICLR 2021,Spotlight | Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples).
This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds.
Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR. | https://openreview.net/pdf/0c626197d75608b562d47b4d7684f620b1359ef5.pdf |
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods | https://openreview.net/forum?id=2m0g1wEafh | https://openreview.net/forum?id=2m0g1wEafh | Taiji Suzuki,Shunta Akiyama | ICLR 2021,Spotlight | Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge regularization on a mildly overparameterized neural network,
and discuss its superiority to a class of linear estimators that includes neural tangent kernel approach, random feature model, other kernel methods, $k$-NN estimator and so on. We consider a teacher-student regression model, and eventually show that {\it any} linear estimator can be outperformed by deep learning in a sense of the minimax optimal rate especially for a high dimension setting. The obtained excess bounds are so-called fast learning rate which is faster than $O(1/\sqrt{n})$ that is obtained by usual Rademacher complexity analysis. This discrepancy is induced by the non-convex geometry of the model and the noisy gradient descent used for neural network training provably reaches a near global optimal solution even though the loss landscape is highly non-convex. Although the noisy gradient descent does not employ any explicit or implicit sparsity inducing regularization, it shows a preferable generalization performance that dominates linear estimators. | https://openreview.net/pdf/9698297c5705cc24f44b95ca9d67c8366730f110.pdf |
Self-supervised Visual Reinforcement Learning with Object-centric Representations | https://openreview.net/forum?id=xppLmXCbOw1 | https://openreview.net/forum?id=xppLmXCbOw1 | Andrii Zadaianchuk,Maximilian Seitzer,Georg Martius | ICLR 2021,Spotlight | Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model.
We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects. | https://openreview.net/pdf/17a02894cb2794484690465818a08e4b35ea6982.pdf |
Discovering a set of policies for the worst case reward | https://openreview.net/forum?id=PUkhWz65dy5 | https://openreview.net/forum?id=PUkhWz65dy5 | Tom Zahavy,Andre Barreto,Daniel J Mankowitz,Shaobo Hou,Brendan O'Donoghue,Iurii Kemaev,Satinder Singh | ICLR 2021,Spotlight | We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward function defined as a linear combination of known features. We consider a specific class of policy compositions which we call set improving policies (SIPs): given a set of policies and a set of tasks, a SIP is any composition of the former whose performance is at least as good as that of its constituents across all the tasks. We focus on the most conservative instantiation of SIPs, set-max policies (SMPs), so our analysis extends to any SIP. This includes known policy-composition operators like generalized policy improvement. Our main contribution is an algorithm that builds a set of policies in order to maximize the worst-case performance of the resulting SMP on the set of tasks. The algorithm works by successively adding new policies to the set. We show that the worst-case performance of the resulting SMP strictly improves at each iteration, and the algorithm only stops when there does not exist a policy that leads to improved performance. We empirically evaluate our algorithm on a grid world and also on a set of domains from the DeepMind control suite. We confirm our theoretical results regarding the monotonically improving performance of our algorithm. Interestingly, we also show empirically that the sets of policies computed by the algorithm are diverse, leading to different trajectories in the grid world and very distinct locomotion skills in the control suite. | https://openreview.net/pdf/7a579d5544cfac14ef616dd30d162838a87cf7e4.pdf |
Implicit Normalizing Flows | https://openreview.net/forum?id=8PS8m9oYtNy | https://openreview.net/forum?id=8PS8m9oYtNy | Cheng Lu,Jianfei Chen,Chongxuan Li,Qiuhao Wang,Jun Zhu | ICLR 2021,Spotlight | Normalizing flows define a probability distribution by an explicit invertible transformation $\boldsymbol{\mathbf{z}}=f(\boldsymbol{\mathbf{x}})$. In this work, we present implicit normalizing flows (ImpFlows), which generalize normalizing flows by allowing the mapping to be implicitly defined by the roots of an equation $F(\boldsymbol{\mathbf{z}}, \boldsymbol{\mathbf{x}})= \boldsymbol{\mathbf{0}}$. ImpFlows build on residual flows (ResFlows) with a proper balance between expressiveness and tractability. Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows. Furthermore, for any ResFlow with a fixed number of blocks, there exists some function that ResFlow has a non-negligible approximation error. However, the function is exactly representable by a single-block ImpFlow. We propose a scalable algorithm to train and draw samples from ImpFlows. Empirically, we evaluate ImpFlow on several classification and density modeling tasks, and ImpFlow outperforms ResFlow with a comparable amount of parameters on all the benchmarks. | https://openreview.net/pdf/19708593079fad9a0012ae316f6d90a49b83c54b.pdf |
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | https://openreview.net/forum?id=Jnspzp-oIZE | https://openreview.net/forum?id=Jnspzp-oIZE | Pim De Haan,Maurice Weiler,Taco Cohen,Max Welling | ICLR 2021,Spotlight | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole. We propose Gauge Equivariant Mesh CNNs which generalize GCNs to apply anisotropic gauge equivariant kernels. Since the resulting features carry orientation information, we introduce a geometric message passing scheme defined by parallel transporting features over mesh edges. Our experiments validate the significantly improved expressivity of the proposed model over conventional GCNs and other methods. | https://openreview.net/pdf/8892729ba223ed386ef20a476c7f878d8329e7b0.pdf |
Generalization bounds via distillation | https://openreview.net/forum?id=EGdFhBzmAwB | https://openreview.net/forum?id=EGdFhBzmAwB | Daniel Hsu,Ziwei Ji,Matus Telgarsky,Lan Wang | ICLR 2021,Spotlight | This paper theoretically investigates the following empirical phenomenon: given a high-complexity network with poor generalization bounds, one can distill it into a network with nearly identical predictions but low complexity and vastly smaller generalization bounds. The main contribution is an analysis showing that the original network inherits this good generalization bound from its distillation, assuming the use of well-behaved data augmentation. This bound is presented both in an abstract and in a concrete form, the latter complemented by a reduction technique to handle modern computation graphs featuring convolutional layers, fully-connected layers, and skip connections, to name a few. To round out the story, a (looser) classical uniform convergence analysis of compression is also presented, as well as a variety of experiments on cifar and mnist demonstrating similar generalization performance between the original network and its distillation.
| https://openreview.net/pdf/dd9e76980b62282e3d975f3ba1c97b2e689d7f50.pdf |
Learning Mesh-Based Simulation with Graph Networks | https://openreview.net/forum?id=roNqYL0_XP | https://openreview.net/forum?id=roNqYL0_XP | Tobias Pfaff,Meire Fortunato,Alvaro Sanchez-Gonzalez,Peter Battaglia | ICLR 2021,Spotlight | Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However, high-dimensional scientific simulations are very expensive to run, and solvers and parameters must often be tuned individually to each system studied.
Here we introduce MeshGraphNets, a framework for learning mesh-based simulations using graph neural networks. Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. Our results show it can accurately predict the dynamics of a wide range of physical systems, including aerodynamics, structural mechanics, and cloth. The model's adaptivity supports learning resolution-independent dynamics and can scale to more complex state spaces at test time. Our method is also highly efficient, running 1-2 orders of magnitude faster than the simulation on which it is trained. Our approach broadens the range of problems on which neural network simulators can operate and promises to improve the efficiency of complex, scientific modeling tasks. | https://openreview.net/pdf/25e22a812f559c7389d64412f32a87195fb7acbb.pdf |
On Statistical Bias In Active Learning: How and When to Fix It | https://openreview.net/forum?id=JiYq3eqTKY | https://openreview.net/forum?id=JiYq3eqTKY | Sebastian Farquhar,Yarin Gal,Tom Rainforth | ICLR 2021,Spotlight | Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weights to remove bias when doing so is beneficial. Through this, our work not only provides a useful mechanism that can improve the active learning approach, but also an explanation for the empirical successes of various existing approaches which ignore this bias. In particular, we show that this bias can be actively helpful when training overparameterized models---like neural networks---with relatively modest dataset sizes. | https://openreview.net/pdf/ed4c09a7b7980f55e8dc837711b624033003c1ba.pdf |
Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic | https://openreview.net/forum?id=LmUJqB1Cz8 | https://openreview.net/forum?id=LmUJqB1Cz8 | Deunsol Yoon,Sunghoon Hong,Byung-Jun Lee,Kee-Eung Kim | ICLR 2021,Spotlight | Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exemplified by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked first in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efficiency in every test scenarios. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids. | https://openreview.net/pdf/19ea8e1953e4da5a00beede7da663e04f6717020.pdf |
Expressive Power of Invariant and Equivariant Graph Neural Networks | https://openreview.net/forum?id=lxHgXYN4bwl | https://openreview.net/forum?id=lxHgXYN4bwl | Waiss Azizian,marc lelarge | ICLR 2021,Spotlight | Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data. In this paper, we propose a theoretical framework able to compare the expressive power of these GNN architectures. The current universality theorems only apply to intractable classes of GNNs. Here, we prove the first approximation guarantees for practical GNNs, paving the way for a better understanding of their generalization. Our theoretical results are proved for invariant GNNs computing a graph embedding (permutation of the nodes of the input graph does not affect the output) and equivariant GNNs computing an embedding of the nodes (permutation of the input permutes the output). We show that Folklore Graph Neural Networks (FGNN), which are tensor based GNNs augmented with matrix multiplication are the most expressive architectures proposed so far for a given tensor order. We illustrate our results on the Quadratic Assignment Problem (a NP-Hard combinatorial problem) by showing that FGNNs are able to learn how to solve the problem, leading to much better average performances than existing algorithms (based on spectral, SDP or other GNNs architectures). On a practical side, we also implement masked tensors to handle batches of graphs of varying sizes. | https://openreview.net/pdf/21b5cfad2a0412a534b651374073f23af8310688.pdf |
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery | https://openreview.net/forum?id=kHSu4ebxFXY | https://openreview.net/forum?id=kHSu4ebxFXY | Yutong Xie,Chence Shi,Hao Zhou,Yuwei Yang,Weinan Zhang,Yong Yu,Lei Li | ICLR 2021,Spotlight | Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing neural models to generate either molecular sequences or chemical graphs. However, it remains a big challenge to find novel and diverse compounds satisfying several properties. In this paper, we propose MARS, a method for multi-objective drug molecule discovery. MARS is based on the idea of generating the chemical candidates by iteratively editing fragments of molecular graphs. To search for high-quality candidates, it employs Markov chain Monte Carlo sampling (MCMC) on molecules with an annealing scheme and an adaptive proposal. To further improve sample efficiency, MARS uses a graph neural network (GNN) to represent and select candidate edits, where the GNN is trained on-the-fly with samples from MCMC. Experiments show that MARS achieves state-of-the-art performance in various multi-objective settings where molecular bio-activity, drug-likeness, and synthesizability are considered. Remarkably, in the most challenging setting where all four objectives are simultaneously optimized, our approach outperforms previous methods significantly in comprehensive evaluations. The code is available at https://github.com/yutxie/mars. | https://openreview.net/pdf/4f5a198cf9191eebd5788dea1fd15fcb151d8ef9.pdf |
On Self-Supervised Image Representations for GAN Evaluation | https://openreview.net/forum?id=NeRdBeTionN | https://openreview.net/forum?id=NeRdBeTionN | Stanislav Morozov,Andrey Voynov,Artem Babenko | ICLR 2021,Spotlight | The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature.
In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading. | https://openreview.net/pdf/7d26090fe6996493b7239c99e76e0f1929984b96.pdf |
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies | https://openreview.net/forum?id=_XYzwxPIQu6 | https://openreview.net/forum?id=_XYzwxPIQu6 | Dominik Schmidt,Georgia Koppe,Zahra Monfared,Max Beutelspacher,Daniel Durstewitz | ICLR 2021,Spotlight | A main theoretical interest in biology and physics is to identify the nonlinear dynamical system (DS) that generated observed time series. Recurrent Neural Networks (RNN) are, in principle, powerful enough to approximate any underlying DS, but in their vanilla form suffer from the exploding vs. vanishing gradients problem. Previous attempts to alleviate this problem resulted either in more complicated, mathematically less tractable RNN architectures, or strongly limited the dynamical expressiveness of the RNN.
Here we address this issue by suggesting a simple regularization scheme for vanilla RNN with ReLU activation which enables them to solve long-range dependency problems and express slow time scales, while retaining a simple mathematical structure which makes their DS properties partly analytically accessible. We prove two theorems that establish a tight connection between the regularized RNN dynamics and their gradients, illustrate on DS benchmarks that our regularization approach strongly eases the reconstruction of DS which harbor widely differing time scales, and show that our method is also en par with other long-range architectures like LSTMs on several tasks. | https://openreview.net/pdf/fb39182856a0c466f6521fc91c26027f3907321b.pdf |
Understanding the role of importance weighting for deep learning | https://openreview.net/forum?id=_WnwtieRHxM | https://openreview.net/forum?id=_WnwtieRHxM | Da Xu,Yuting Ye,Chuanwei Ruan | ICLR 2021,Spotlight | The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models. They observe that as long as the model can separate the training data, the impact of importance weighting diminishes as the training proceeds. Nevertheless, there lacks a rigorous characterization of this phenomenon. In this paper, we provide formal characterizations and theoretical justifications on the role of importance weighting with respect to the implicit bias of gradient descent and margin-based learning theory. We reveal both the optimization dynamics and generalization performance under deep learning models. Our work not only explains the various novel phenomenons observed for importance weighting in deep learning, but also extends to the studies where the weights are being optimized as part of the model, which applies to a number of topics under active research. | https://openreview.net/pdf/c52af0c839abd3f9cd52515da1042deed9eb2692.pdf |
RMSprop converges with proper hyper-parameter | https://openreview.net/forum?id=3UDSdyIcBDA | https://openreview.net/forum?id=3UDSdyIcBDA | Naichen Shi,Dawei Li,Mingyi Hong,Ruoyu Sun | ICLR 2021,Spotlight | Despite the existence of divergence examples, RMSprop remains
one of the most popular algorithms in machine learning. Towards closing the gap between theory and practice, we prove that RMSprop converges with proper choice of hyper-parameters under certain conditions. More specifically, we prove that when the hyper-parameter $\beta_2$ is close enough to $1$, RMSprop and its random shuffling version converge to a bounded region in general, and to critical points in the interpolation regime. It is worth mentioning that our results do not depend on ``bounded gradient" assumption, which is often the key assumption utilized by existing theoretical work for Adam-type adaptive gradient method. Removing this assumption allows us to establish a phase transition from divergence to non-divergence for RMSprop.
Finally, based on our theory, we conjecture that in practice there is a critical threshold $\sf{\beta_2^*}$, such that RMSprop generates reasonably good results only if $1>\beta_2\ge \sf{\beta_2^*}$. We provide empirical evidence for such a phase transition in our numerical experiments. | https://openreview.net/pdf/3df947e54dae8ab86ecfdcbf1ee7cc3542fe9e76.pdf |
The Intrinsic Dimension of Images and Its Impact on Learning | https://openreview.net/forum?id=XJk19XzGq2J | https://openreview.net/forum?id=XJk19XzGq2J | Phil Pope,Chen Zhu,Ahmed Abdelkader,Micah Goldblum,Tom Goldstein | ICLR 2021,Spotlight | It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common intuition for the remarkable success of deep learning in computer vision. In this work, we apply dimension estimation tools to popular datasets and investigate the role of low-dimensional structure in deep learning. We find that common natural image datasets indeed have very low intrinsic dimension relative to the high number of pixels in the images. Additionally, we find that low dimensional datasets are easier for neural networks to learn, and models solving these tasks generalize better from training to test data. Along the way, we develop a technique for validating our dimension estimation tools on synthetic data generated by GANs allowing us to actively manipulate the intrinsic dimension by controlling the image generation process. Code for our experiments may be found \href{https://github.com/ppope/dimensions}{here}. | https://openreview.net/pdf/37e9d9b97958a37df9ebdb5cb318f8ddab3841f3.pdf |
Learning with Feature-Dependent Label Noise: A Progressive Approach | https://openreview.net/forum?id=ZPa2SyGcbwh | https://openreview.net/forum?id=ZPa2SyGcbwh | Yikai Zhang,Songzhu Zheng,Pengxiang Wu,Mayank Goswami,Chao Chen | ICLR 2021,Spotlight | Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic without theoretical guarantees. In this paper, we propose to target a new family of feature-dependent label noise, which is much more general than commonly used i.i.d. label noise and encompasses a broad spectrum of noise patterns. Focusing on this general noise family, we propose a progressive label correction algorithm that iteratively corrects labels and refines the model. We provide theoretical guarantees showing that for a wide variety of (unknown) noise patterns, a classifier trained with this strategy converges to be consistent with the Bayes classifier. In experiments, our method outperforms SOTA baselines and is robust to various noise types and levels. | https://openreview.net/pdf/2223330f69a5f2c06d8e54f79e3935f8da43de13.pdf |
Disentangled Recurrent Wasserstein Autoencoder | https://openreview.net/forum?id=O7ms4LFdsX | https://openreview.net/forum?id=O7ms4LFdsX | Jun Han,Martin Renqiang Min,Ligong Han,Li Erran Li,Xuan Zhang | ICLR 2021,Spotlight | Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework. However, only a few works have explored unsupervised disentangled sequential representation learning due to challenges of generating sequential data. In this paper, we propose recurrent Wasserstein Autoencoder (R-WAE), a new framework for generative modeling of sequential data. R-WAE disentangles the representation of an input sequence into static and dynamic factors (i.e., time-invariant and time-varying parts). Our theoretical analysis shows that, R-WAE minimizes an upper bound of a penalized form of the Wasserstein distance between model distribution and sequential data distribution, and simultaneously maximizes the mutual information between input data and different disentangled latent factors, respectively. This is superior to (recurrent) VAE which does not explicitly enforce mutual information maximization between input data and disentangled latent representations. When the number of actions in sequential data is available as weak supervision information, R-WAE is extended to learn a categorical latent representation of actions to improve its disentanglement. Experiments on a variety of datasets show that our models outperform other baselines with the same settings in terms of disentanglement and unconditional video generation both quantitatively and qualitatively. | https://openreview.net/pdf/e8de83dfa5c5849037a74c858c899119d1dc5f33.pdf |
Influence Estimation for Generative Adversarial Networks | https://openreview.net/forum?id=opHLcXxYTC_ | https://openreview.net/forum?id=opHLcXxYTC_ | Naoyuki Terashita,Hiroki Ohashi,Yuichi Nonaka,Takashi Kanemaru | ICLR 2021,Spotlight | Identifying harmful instances, whose absence in a training dataset improves model performance, is important for building better machine learning models.
Although previous studies have succeeded in estimating harmful instances under supervised settings, they cannot be trivially extended to generative adversarial networks (GANs).
This is because previous approaches require that (i) the absence of a training instance directly affects the loss value and that (ii) the change in the loss directly measures the harmfulness of the instance for the performance of a model.
In GAN training, however, neither of the requirements is satisfied.
This is because, (i) the generator’s loss is not directly affected by the training instances as they are not part of the generator's training steps, and (ii) the values of GAN's losses normally do not capture the generative performance of a model.
To this end, (i) we propose an influence estimation method that uses the Jacobian of the gradient of the generator's loss with respect to the discriminator’s parameters (and vice versa) to trace how the absence of an instance in the discriminator’s training affects the generator’s parameters, and (ii) we propose a novel evaluation scheme, in which we assess harmfulness of each training instance on the basis of how GAN evaluation metric (e.g., inception score) is expected to change due to the removal of the instance.
We experimentally verified that our influence estimation method correctly inferred the changes in GAN evaluation metrics.
We also demonstrated that the removal of the identified harmful instances effectively improved the model’s generative performance with respect to various GAN evaluation metrics. | https://openreview.net/pdf/f9fcbfb06cf86dc39ab1289d0a9f9febb65a8ac9.pdf |
Orthogonalizing Convolutional Layers with the Cayley Transform | https://openreview.net/forum?id=Pbj8H_jEHYv | https://openreview.net/forum?id=Pbj8H_jEHYv | Asher Trockman,J Zico Kolter | ICLR 2021,Spotlight | Recent work has highlighted several advantages of enforcing orthogonality in the weight layers of deep networks, such as maintaining the stability of activations, preserving gradient norms, and enhancing adversarial robustness by enforcing low Lipschitz constants. Although numerous methods exist for enforcing the orthogonality of fully-connected layers, those for convolutional layers are more heuristic in nature, often focusing on penalty methods or limited classes of convolutions. In this work, we propose and evaluate an alternative approach to directly parameterize convolutional layers that are constrained to be orthogonal. Specifically, we propose to apply the Cayley transform to a skew-symmetric convolution in the Fourier domain, so that the inverse convolution needed by the Cayley transform can be computed efficiently. We compare our method to previous Lipschitz-constrained and orthogonal convolutional layers and show that it indeed preserves orthogonality to a high degree even for large convolutions. Applied to the problem of certified adversarial robustness, we show that networks incorporating the layer outperform existing deterministic methods for certified defense against $\ell_2$-norm-bounded adversaries, while scaling to larger architectures than previously investigated. Code is available at https://github.com/locuslab/orthogonal-convolutions. | https://openreview.net/pdf/6ae88022a0cffd7671fd9602be34926437a0f6cb.pdf |
Predicting Infectiousness for Proactive Contact Tracing | https://openreview.net/forum?id=lVgB2FUbzuQ | https://openreview.net/forum?id=lVgB2FUbzuQ | Yoshua Bengio,Prateek Gupta,Tegan Maharaj,Nasim Rahaman,Martin Weiss,Tristan Deleu,Eilif Benjamin Muller,Meng Qu,victor schmidt,Pierre-Luc St-Charles,hannah alsdurf,Olexa Bilaniuk,david buckeridge,gaetan caron,pierre luc carrier,Joumana Ghosn,satya ortiz gagne,Christopher Pal,Irina Rish,Bernhard Schölkopf,abhinav sharma,Jian Tang,andrew williams | ICLR 2021,Spotlight | The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs be-tween privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual’s test results, with corresponding binary recommendations that either all or none of the individual’s contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual’s infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual’s contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). Similarly to other works, we find that compared to no tracing, all DCT methods tested are able to reduce spread of the disease and thus save lives, even at low adoption rates, strongly supporting a role for DCT methods in managing the pandemic. Further, we find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention. | https://openreview.net/pdf/77dc30fa1394d3f1fb24031ef8b391fa4d51c35d.pdf |
Learning a Latent Simplex in Input Sparsity Time | https://openreview.net/forum?id=04LZCAxMSco | https://openreview.net/forum?id=04LZCAxMSco | Ainesh Bakshi,Chiranjib Bhattacharyya,Ravi Kannan,David Woodruff,Samson Zhou | ICLR 2021,Spotlight | We consider the problem of learning a latent $k$-vertex simplex $K\in\mathbb{R}^d$, given $\mathbf{A}\in\mathbb{R}^{d\times n}$, which can be viewed as $n$ data points that are formed by randomly perturbing some latent points in $K$, possibly beyond $K$. A large class of latent variable models, such as adversarial clustering, mixed membership stochastic block models, and topic models can be cast in this view of learning a latent simplex. Bhattacharyya and Kannan (SODA 2020) give an algorithm for learning such a $k$-vertex latent simplex in time roughly $O(k\cdot\text{nnz}(\mathbf{A}))$, where $\text{nnz}(\mathbf{A})$ is the number of non-zeros in $\mathbf{A}$. We show that the dependence on $k$ in the running time is unnecessary given a natural assumption about the mass of the top $k$ singular values of $\mathbf{A}$, which holds in many of these applications. Further, we show this assumption is necessary, as otherwise an algorithm for learning a latent simplex would imply a better low rank approximation algorithm than what is known.
We obtain a spectral low-rank approximation to $\mathbf{A}$ in input-sparsity time and show that the column space thus obtained has small $\sin\Theta$ (angular) distance to the right top-$k$ singular space of $\mathbf{A}$. Our algorithm then selects $k$ points in the low-rank subspace with the largest inner product (in absolute value) with $k$ carefully chosen random vectors. By working in the low-rank subspace, we avoid reading the entire matrix in each iteration and thus circumvent the $\Theta(k\cdot\text{nnz}(\mathbf{A}))$ running time. | https://openreview.net/pdf/f27cd4887ef1ff6feb1973dc1b610ff04bfe30d3.pdf |
CPT: Efficient Deep Neural Network Training via Cyclic Precision | https://openreview.net/forum?id=87ZwsaQNHPZ | https://openreview.net/forum?id=87ZwsaQNHPZ | Yonggan Fu,Han Guo,Meng Li,Xin Yang,Yining Ding,Vikas Chandra,Yingyan Lin | ICLR 2021,Spotlight | Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding DNN training: we conjecture that DNNs' precision might have a similar effect as the learning rate during DNN training, and advocate dynamic precision along the training trajectory for further boosting the time/energy efficiency of DNN training. Specifically, we propose Cyclic Precision Training (CPT) to cyclically vary the precision between two boundary values which can be identified using a simple precision range test within the first few training epochs. Extensive simulations and ablation studies on five datasets and eleven models demonstrate that CPT's effectiveness is consistent across various models/tasks (including classification and language modeling). Furthermore, through experiments and visualization we show that CPT helps to (1) converge to a wider minima with a lower generalization error and (2) reduce training variance which we believe opens up a new design knob for simultaneously improving the optimization and efficiency of DNN training. | https://openreview.net/pdf/2f7dc996e355dc20f6d4b64c9b9cf272cfac9117.pdf |
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration | https://openreview.net/forum?id=w_7JMpGZRh0 | https://openreview.net/forum?id=w_7JMpGZRh0 | Xavier Puig,Tianmin Shu,Shuang Li,Zilin Wang,Yuan-Hong Liao,Joshua B. Tenenbaum,Sanja Fidler,Antonio Torralba | ICLR 2021,Spotlight | In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. We evaluate the performance of AI agents with the human-like agent as well as and with real humans using objective metrics and subjective user ratings. Experimental results demonstrate that our challenge and virtual environment enable a systematic evaluation on the important aspects of machine social intelligence at scale. | https://openreview.net/pdf/9bfeb3924965335d86eb7bb9552109e716b83e8a.pdf |
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | https://openreview.net/forum?id=F1vEjWK-lH_ | https://openreview.net/forum?id=F1vEjWK-lH_ | Zirui Wang,Yulia Tsvetkov,Orhan Firat,Yuan Cao | ICLR 2021,Spotlight | Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem structure for improving optimization efficiency remains under-explored. In this paper, we attempt to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with not only language proximity but also the overall model performance. Such observation helps us to identify a critical limitation of existing gradient-based multi-task learning methods, and thus we derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks. Empirically, our method obtains significant model performance gains on multilingual machine translation and XTREME benchmark tasks for multilingual language models. Our work reveals the importance of properly measuring and utilizing language proximity in multilingual optimization, and has broader implications for multi-task learning beyond multilingual modeling. | https://openreview.net/pdf/4958372042631716242a4b3f1a10231548614522.pdf |
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics | https://openreview.net/forum?id=xCcdBRQEDW | https://openreview.net/forum?id=xCcdBRQEDW | Zhiao Huang,Yuanming Hu,Tao Du,Siyuan Zhou,Hao Su,Joshua B. Tenenbaum,Chuang Gan | ICLR 2021,Spotlight | Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only simulate rigid body physics. Additionally, the simulation process usually does not provide gradients that might be useful for planning and control optimizations. We introduce a new differentiable physics benchmark called PasticineLab, which includes a diverse collection of soft body manipulation tasks. In each task, the agent uses manipulators to deform the plasticine into a desired configuration. The underlying physics engine supports differentiable elastic and plastic deformation using the DiffTaichi system, posing many under-explored challenges to robotic agents. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark. Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning. We expect that PlasticineLab will encourage the development of novel algorithms that combine differentiable physics and RL for more complex physics-based skill learning tasks. PlasticineLab will be made publicly available. | https://openreview.net/pdf/9e12adbc865507edd6c3c49f14880127967bfea8.pdf |
Distributional Sliced-Wasserstein and Applications to Generative Modeling | https://openreview.net/forum?id=QYjO70ACDK | https://openreview.net/forum?id=QYjO70ACDK | Khai Nguyen,Nhat Ho,Tung Pham,Hung Bui | ICLR 2021,Spotlight | Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space. However, SW requires many unnecessary projection samples to approximate its value while Max-SW only uses the most important projection, which ignores the information of other useful directions. In order to account for these weaknesses, we propose a novel distance, named Distributional Sliced-Wasserstein distance (DSW), that finds an optimal distribution over projections that can balance between exploring distinctive projecting directions and the informativeness of projections themselves. We show that the DSW is a generalization of Max-SW, and it can be computed efficiently by searching for the optimal push-forward measure over a set of probability measures over the unit sphere satisfying certain regularizing constraints that favor distinct directions. Finally, we conduct extensive experiments with large-scale datasets to demonstrate the favorable performances of the proposed distances over the previous sliced-based distances in generative modeling applications. | https://openreview.net/pdf/c1fe6afe9b88f261b6452d8a2c078c7ac57ebc41.pdf |
Systematic generalisation with group invariant predictions | https://openreview.net/forum?id=b9PoimzZFJ | https://openreview.net/forum?id=b9PoimzZFJ | Faruk Ahmed,Yoshua Bengio,Harm van Seijen,Aaron Courville | ICLR 2021,Spotlight | We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently correlating complex features. When the non-persistent, simpler correlations correspond to non-semantic background factors, a neural network trained on this data can exhibit dramatic failure upon encountering systematic distributional shift, where the correlating background features are recombined with different objects. We perform an empirical study on three synthetic datasets, showing that group invariance methods across inferred partitionings of the training set can lead to significant improvements at such test-time situations. We also suggest a simple invariance penalty, showing with experiments on our setups that it can perform better than alternatives. We find that even without assuming access to any systematically shifted validation sets, one can still find improvements over an ERM-trained reference model. | https://openreview.net/pdf/245d90bc6bda1f04d5147ba12d683ab7d6b4d243.pdf |
Memory Optimization for Deep Networks | https://openreview.net/forum?id=bnY0jm4l59 | https://openreview.net/forum?id=bnY0jm4l59 | Aashaka Shah,Chao-Yuan Wu,Jayashree Mohan,Vijay Chidambaram,Philipp Kraehenbuehl | ICLR 2021,Spotlight | Deep learning is slowly, but steadily, hitting a memory bottleneck. While the tensor computation in top-of-the-line GPUs increased by $32\times$ over the last five years, the total available memory only grew by $2.5\times$. This prevents researchers from exploring larger architectures, as training large networks requires more memory for storing intermediate outputs. In this paper, we present MONeT, an automatic framework that minimizes both the memory footprint and computational overhead of deep networks. MONeT jointly optimizes the checkpointing schedule and the implementation of various operators. MONeT is able to outperform all prior hand-tuned operations as well as automated checkpointing. MONeT reduces the overall memory requirement by $3\times$ for various PyTorch models, with a 9-16$\%$ overhead in computation. For the same computation cost, MONeT requires 1.2-1.8$\times$ less memory than current state-of-the-art automated checkpointing frameworks. Our code will be made publicly available upon acceptance. | https://openreview.net/pdf/c229005882f26be8587687dbfc967c17b75d1bac.pdf |
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark | https://openreview.net/forum?id=_0kaDkv3dVf | https://openreview.net/forum?id=_0kaDkv3dVf | Chaojian Li,Zhongzhi Yu,Yonggan Fu,Yongan Zhang,Yang Zhao,Haoran You,Qixuan Yu,Yue Wang,Cong Hao,Yingyan Lin | ICLR 2021,Spotlight | HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of deep neural networks deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. The former can be time-consuming due to the required knowledge of the device’s compilation method and how to set up the measurement pipeline, while building the latter is often a barrier for non-hardware experts like NAS researchers. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e.g., energy cost and latency) of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying our pre-measured dataset and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench. | https://openreview.net/pdf/1256d25521d41df912407cdd9aea52fa6d3d5265.pdf |
Regularized Inverse Reinforcement Learning | https://openreview.net/forum?id=HgLO8yalfwc | https://openreview.net/forum?id=HgLO8yalfwc | Wonseok Jeon,Chen-Yang Su,Paul Barde,Thang Doan,Derek Nowrouzezahrai,Joelle Pineau | ICLR 2021,Spotlight | Inverse Reinforcement Learning (IRL) aims to facilitate a learner’s ability to imitate expert behavior by acquiring reward functions that explain the expert’s decisions. Regularized IRLapplies strongly convex regularizers to the learner’s policy in order to avoid the expert’s behavior being rationalized by arbitrary constant rewards, also known as degenerate solutions. We propose tractable solutions, and practical methods to obtain them, for regularized IRL. Current methods are restricted to the maximum-entropy IRL framework, limiting them to Shannon-entropy regularizers, as well as proposing solutions that are intractable in practice. We present theoretical backing for our proposed IRL method’s applicability to both discrete and continuous controls, empirically validating our performance on a variety of tasks. | https://openreview.net/pdf/fdd6861c0f1a3c729dc7a2d59344633293d0df3e.pdf |
What are the Statistical Limits of Offline RL with Linear Function Approximation? | https://openreview.net/forum?id=30EvkP2aQLD | https://openreview.net/forum?id=30EvkP2aQLD | Ruosong Wang,Dean Foster,Sham M. Kakade | ICLR 2021,Spotlight | Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions.
This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: i) we have realizability in that the true value function of \emph{every} policy is linear in a given set of features and 2) our off-policy data has good coverage over all features (under a strong spectral condition), any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon to non-trivially estimate the value of \emph{any} given policy. Our results highlight that sample-efficient offline policy evaluation is not possible unless significantly stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or significantly stronger representational conditions (beyond realizability). | https://openreview.net/pdf/abc8c212aff033c6042f681a3e27a3c9af250066.pdf |
Behavioral Cloning from Noisy Demonstrations | https://openreview.net/forum?id=zrT3HcsWSAt | https://openreview.net/forum?id=zrT3HcsWSAt | Fumihiro Sasaki,Ryota Yamashina | ICLR 2021,Spotlight | We consider the problem of learning an optimal expert behavior policy given noisy demonstrations that contain observations from both optimal and non-optimal expert behaviors. Popular imitation learning algorithms, such as generative adversarial imitation learning, assume that (clear) demonstrations are given from optimal expert policies but not the non-optimal ones, and thus often fail to imitate the optimal expert behaviors given the noisy demonstrations. Prior works that address the problem require (1) learning policies through environment interactions in the same fashion as reinforcement learning, and (2) annotating each demonstration with confidence scores or rankings. However, such environment interactions and annotations in real-world settings take impractically long training time and a significant human effort. In this paper, we propose an imitation learning algorithm to address the problem without any environment interactions and annotations associated with the non-optimal demonstrations. The proposed algorithm learns ensemble policies with a generalized behavioral cloning (BC) objective function where we exploit another policy already learned by BC. Experimental results show that the proposed algorithm can learn behavior policies that are much closer to the optimal policies than ones learned by BC. | https://openreview.net/pdf/980d70256a0232aceda73c88d52522e48fff995d.pdf |
How Does Mixup Help With Robustness and Generalization? | https://openreview.net/forum?id=8yKEo06dKNo | https://openreview.net/forum?id=8yKEo06dKNo | Linjun Zhang,Zhun Deng,Kenji Kawaguchi,Amirata Ghorbani,James Zou | ICLR 2021,Spotlight | Mixup is a popular data augmentation technique based on on convex combinations of pairs of examples and their labels. This simple technique has shown to substantially improve both the model's robustness as well as the generalization of the trained model. However, it is not well-understood why such improvement occurs. In this paper, we provide theoretical analysis to demonstrate how using Mixup in training helps model robustness and generalization. For robustness, we show that minimizing the Mixup loss corresponds to approximately minimizing an upper bound of the adversarial loss. This explains why models obtained by Mixup training exhibits robustness to several kinds of adversarial attacks such as Fast Gradient Sign Method (FGSM). For generalization, we prove that Mixup augmentation corresponds to a specific type of data-adaptive regularization which reduces overfitting. Our analysis provides new insights and a framework to understand Mixup.
| https://openreview.net/pdf/3f1f8d26c6d14576d3b00d41ecdf274465cf649b.pdf |
Emergent Symbols through Binding in External Memory | https://openreview.net/forum?id=LSFCEb3GYU7 | https://openreview.net/forum?id=LSFCEb3GYU7 | Taylor Whittington Webb,Ishan Sinha,Jonathan Cohen | ICLR 2021,Spotlight | A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience. Deep neural network algorithms have proven to be a powerful tool for learning directly from high-dimensional data, but currently lack this capacity for data-efficient induction of abstract rules, leading some to argue that symbol-processing mechanisms will be necessary to account for this capacity. In this work, we take a step toward bridging this gap by introducing the Emergent Symbol Binding Network (ESBN), a recurrent network augmented with an external memory that enables a form of variable-binding and indirection. This binding mechanism allows symbol-like representations to emerge through the learning process without the need to explicitly incorporate symbol-processing machinery, enabling the ESBN to learn rules in a manner that is abstracted away from the particular entities to which those rules apply. Across a series of tasks, we show that this architecture displays nearly perfect generalization of learned rules to novel entities given only a limited number of training examples, and outperforms a number of other competitive neural network architectures. | https://openreview.net/pdf/215292886f63402e04c31a7364d525407cb5b2ad.pdf |
Individually Fair Gradient Boosting | https://openreview.net/forum?id=JBAa9we1AL | https://openreview.net/forum?id=JBAa9we1AL | Alexander Vargo,Fan Zhang,Mikhail Yurochkin,Yuekai Sun | ICLR 2021,Spotlight | We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias. | https://openreview.net/pdf/edda3e5c0ee042a5a03ebfdd14665355f34cd023.pdf |
Structured Prediction as Translation between Augmented Natural Languages | https://openreview.net/forum?id=US-TP-xnXI | https://openreview.net/forum?id=US-TP-xnXI | Giovanni Paolini,Ben Athiwaratkun,Jason Krone,Jie Ma,Alessandro Achille,RISHITA ANUBHAI,Cicero Nogueira dos Santos,Bing Xiang,Stefano Soatto | ICLR 2021,Spotlight | We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. Instead of tackling the problem by training task-specific discriminative classifiers, we frame it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. Our approach can match or outperform task-specific models on all tasks, and in particular achieves new state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while using the same architecture and hyperparameters for all tasks, and even when training a single model to solve all tasks at the same time (multi-task learning). Finally, we show that our framework can also significantly improve the performance in a low-resource regime, thanks to better use of label semantics. | https://openreview.net/pdf/6d920934176b3cc6eddc9d925f48b786787c9a92.pdf |
Minimum Width for Universal Approximation | https://openreview.net/forum?id=O-XJwyoIF-k | https://openreview.net/forum?id=O-XJwyoIF-k | Sejun Park,Chulhee Yun,Jaeho Lee,Jinwoo Shin | ICLR 2021,Spotlight | The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks. However, the critical width enabling the universal approximation has not been exactly characterized in terms of the input dimension $d_x$ and the output dimension $d_y$. In this work, we provide the first definitive result in this direction for networks using the ReLU activation functions: The minimum width required for the universal approximation of the $L^p$ functions is exactly $\max\{d_x+1,d_y\}$. We also prove that the same conclusion does not hold for the uniform approximation with ReLU, but does hold with an additional threshold activation function. Our proof technique can be also used to derive a tighter upper bound on the minimum width required for the universal approximation using networks with general activation functions. | https://openreview.net/pdf/e18b24a6733ce47aa4779ab76eb962fe09a49a45.pdf |
Topology-Aware Segmentation Using Discrete Morse Theory | https://openreview.net/forum?id=LGgdb4TS4Z | https://openreview.net/forum?id=LGgdb4TS4Z | Xiaoling Hu,Yusu Wang,Li Fuxin,Dimitris Samaras,Chao Chen | ICLR 2021,Spotlight | In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel connectivity and membrane closure, is crucial for downstream analysis tasks. In this paper, we propose a new approach to train deep image segmentation networks for better topological accuracy. In particular, leveraging the power of discrete Morse theory (DMT), we identify global structures, including 1D skeletons and 2D patches, which are important for topological accuracy. Trained with a novel loss based on these global structures, the network performance is significantly improved especially near topologically challenging locations (such as weak spots of connections and membranes). On diverse datasets, our method achieves superior performance on both the DICE score and topological metrics. | https://openreview.net/pdf/e328a81d734d6f9eb2808b309985c8aeffb1b27b.pdf |
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking | https://openreview.net/forum?id=WznmQa42ZAx | https://openreview.net/forum?id=WznmQa42ZAx | Michael Sejr Schlichtkrull,Nicola De Cao,Ivan Titov | ICLR 2021,Spotlight | Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically on understanding which parts of the graphs (e.g. syntactic trees or co-reference structures) contribute to a prediction. In this work, we introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. Given a trained GNN model, we learn a simple classifier that, for every edge in every layer, predicts if that edge can be dropped. We demonstrate that such a classifier can be trained in a fully differentiable fashion, employing stochastic gates and encouraging sparsity through the expected $L_0$ norm. We use our technique as an attribution method to analyze GNN models for two tasks -- question answering and semantic role labeling -- providing insights into the information flow in these models. We show that we can drop a large proportion of edges without deteriorating the performance of the model, while we can analyse the remaining edges for interpreting model predictions. | https://openreview.net/pdf/57426959ac4fb7404f8b571713efb9795322ed3d.pdf |
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control | https://openreview.net/forum?id=yr1mzrH3IC | https://openreview.net/forum?id=yr1mzrH3IC | Zhuang Liu,Xuanlin Li,Bingyi Kang,Trevor Darrell | ICLR 2021,Spotlight | Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment, and because the deep RL community focuses more on high-level algorithm designs. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement, especially on harder tasks. Our findings are shown to be robust against training hyperparameter variations. We also compare these techniques with the more widely used entropy regularization. In addition, we study regularizing different components and find that only regularizing the policy network is typically the best. We further analyze why regularization may help generalization in RL from four perspectives - sample complexity, reward distribution, weight norm, and noise robustness. We hope our study provides guidance for future practices in regularizing policy optimization algorithms. Our code is available at https://github.com/xuanlinli17/iclr2021_rlreg . | https://openreview.net/pdf/4ea87f9a661822f24e9371710f2f815e90d8bd23.pdf |
Recurrent Independent Mechanisms | https://openreview.net/forum?id=mLcmdlEUxy- | https://openreview.net/forum?id=mLcmdlEUxy- | Anirudh Goyal,Alex Lamb,Jordan Hoffmann,Shagun Sodhani,Sergey Levine,Yoshua Bengio,Bernhard Schölkopf | ICLR 2021,Spotlight | We explore the hypothesis that learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes that only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate with nearly independent transition dynamics, communicate only sparingly through the bottleneck of attention, and compete with each other so they are updated only at time steps where they are most relevant. We show that this leads to specialization amongst the RIMs, which in turn allows for remarkably improved generalization on tasks where some factors of variation differ systematically between training and evaluation.
| https://openreview.net/pdf/a58297a8b9fc47b94fee061cf119f991c6bb2a87.pdf |
Dataset Inference: Ownership Resolution in Machine Learning | https://openreview.net/forum?id=hvdKKV2yt7T | https://openreview.net/forum?id=hvdKKV2yt7T | Pratyush Maini,Mohammad Yaghini,Nicolas Papernot | ICLR 2021,Spotlight | With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property. This has spurred interest in model stealing, which is made more practical by advances in learning with partial, little, or no supervision. Existing defenses focus on inserting unique watermarks in a model's decision surface, but this is insufficient: the watermarks are not sampled from the training distribution and thus are not always preserved during model stealing. In this paper, we make the key observation that knowledge contained in the stolen model's training set is what is common to all stolen copies. The adversary's goal, irrespective of the attack employed, is always to extract this knowledge or its by-products. This gives the original model's owner a strong advantage over the adversary: model owners have access to the original training data. We thus introduce $\textit{dataset inference}$, the process of identifying whether a suspected model copy has private knowledge from the original model's dataset, as a defense against model stealing. We develop an approach for dataset inference that combines statistical testing with the ability to estimate the distance of multiple data points to the decision boundary. Our experiments on CIFAR10, SVHN, CIFAR100 and ImageNet show that model owners can claim with confidence greater than 99% that their model (or dataset as a matter of fact) was stolen, despite only exposing 50 of the stolen model's training points. Dataset inference defends against state-of-the-art attacks even when the adversary is adaptive. Unlike prior work, it does not require retraining or overfitting the defended model. | https://openreview.net/pdf/f677fca9fd0a50d90120a4a823fcbbe889d8ca28.pdf |
Learning from Protein Structure with Geometric Vector Perceptrons | https://openreview.net/forum?id=1YLJDvSx6J4 | https://openreview.net/forum?id=1YLJDvSx6J4 | Bowen Jing,Stephan Eismann,Patricia Suriana,Raphael John Lamarre Townshend,Ron Dror | ICLR 2021,Spotlight | Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the geometric and relational aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors. Graph neural networks equipped with such layers are able to perform both geometric and relational reasoning on efficient representations of macromolecules. We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design. Our approach improves over existing classes of architectures on both problems, including state-of-the-art convolutional neural networks and graph neural networks. We release our code at https://github.com/drorlab/gvp. | https://openreview.net/pdf/9f3a4c7663dfbd524333bd5b864a86b7840e5808.pdf |
Unsupervised Object Keypoint Learning using Local Spatial Predictability | https://openreview.net/forum?id=GJwMHetHc73 | https://openreview.net/forum?id=GJwMHetHc73 | Anand Gopalakrishnan,Sjoerd van Steenkiste,Jürgen Schmidhuber | ICLR 2021,Spotlight | We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, it utilizes predictability to discover object keypoints, an intrinsic property of objects. This ensures that it does not overly bias keypoints to focus on characteristics that are not unique to objects, such as movement, shape, colour etc. We demonstrate the efficacy of PermaKey on Atari where it learns keypoints corresponding to the most salient object parts and is robust to certain visual distractors. Further, on downstream RL tasks in the Atari domain we demonstrate how agents equipped with our keypoints outperform those using competing alternatives, even on challenging environments with moving backgrounds or distractor objects.
| https://openreview.net/pdf/9c46c3c76f9646f35cf6acdbca761d23e88bde59.pdf |
Fast Geometric Projections for Local Robustness Certification | https://openreview.net/forum?id=zWy1uxjDdZJ | https://openreview.net/forum?id=zWy1uxjDdZJ | Aymeric Fromherz,Klas Leino,Matt Fredrikson,Bryan Parno,Corina Pasareanu | ICLR 2021,Spotlight | Local robustness ensures that a model classifies all inputs within an $\ell_p$-ball consistently, which precludes various forms of adversarial inputs.
In this paper, we present a fast procedure for checking local robustness in feed-forward neural networks with piecewise-linear activation functions.
Such networks partition the input space into a set of convex polyhedral regions in which the network’s behavior is linear;
hence, a systematic search for decision boundaries within the regions around a given input is sufficient for assessing robustness.
Crucially, we show how the regions around a point can be analyzed using simple geometric projections, thus admitting an efficient, highly-parallel GPU implementation that excels particularly for the $\ell_2$ norm, where previous work has been less effective.
Empirically we find this approach to be far more precise than many approximate verification approaches, while at the same time performing multiple orders of magnitude faster than complete verifiers, and scaling to much deeper networks. | https://openreview.net/pdf/e7c0f94bb4ccdfc83c89d5c2f85953aa73ab32b8.pdf |
Random Feature Attention | https://openreview.net/forum?id=QtTKTdVrFBB | https://openreview.net/forum?id=QtTKTdVrFBB | Hao Peng,Nikolaos Pappas,Dani Yogatama,Roy Schwartz,Noah Smith,Lingpeng Kong | ICLR 2021,Spotlight | Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep. While attention is powerful, it does not scale efficiently to long sequences due to its quadratic time and space complexity in the sequence length. We propose RFA, a linear time and space attention that uses random feature methods to approximate the softmax function, and explore its application in transformers. RFA can be used as a drop-in replacement for conventional softmax attention and offers a straightforward way of learning with recency bias through an optional gating mechanism. Experiments on language modeling and machine translation demonstrate that RFA achieves similar or better performance compared to strong transformer baselines. In the machine translation experiment, RFA decodes twice as fast as a vanilla transformer. Compared to existing efficient transformer variants, RFA is competitive in terms of both accuracy and efficiency on three long text classification datasets. Our analysis shows that RFA’s efficiency gains are especially notable on long sequences, suggesting that RFA will be particularly useful in tasks that require working with large inputs, fast decoding speed, or low memory footprints. | https://openreview.net/pdf/3066e95a6460c5d1da53125f5cff04e2d4ad6c4a.pdf |
Sharpness-aware Minimization for Efficiently Improving Generalization | https://openreview.net/forum?id=6Tm1mposlrM | https://openreview.net/forum?id=6Tm1mposlrM | Pierre Foret,Ariel Kleiner,Hossein Mobahi,Behnam Neyshabur | ICLR 2021,Spotlight | In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality. Motivated by the connection between geometry of the loss landscape and generalization---including a generalization bound that we prove here---we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness. In particular, our procedure, Sharpness-Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this formulation results in a min-max optimization problem on which gradient descent can be performed efficiently. We present empirical results showing that SAM improves model generalization across a variety of benchmark datasets (e.g., CIFAR-{10, 100}, ImageNet, finetuning tasks) and models, yielding novel state-of-the-art performance for several. Additionally, we find that SAM natively provides robustness to label noise on par with that provided by state-of-the-art procedures that specifically target learning with noisy labels. | https://openreview.net/pdf/6381fb621c36eb2213f5ef29237a9b4bb75eb839.pdf |
PMI-Masking: Principled masking of correlated spans | https://openreview.net/forum?id=3Aoft6NWFej | https://openreview.net/forum?id=3Aoft6NWFej | Yoav Levine,Barak Lenz,Opher Lieber,Omri Abend,Kevin Leyton-Brown,Moshe Tennenholtz,Yoav Shoham | ICLR 2021,Spotlight | Masking tokens uniformly at random constitutes a common flaw in the pretraining of Masked Language Models (MLMs) such as BERT. We show that such uniform masking allows an MLM to minimize its training objective by latching onto shallow local signals, leading to pretraining inefficiency and suboptimal downstream performance. To address this flaw, we propose PMI-Masking, a principled masking strategy based on the concept of Pointwise Mutual Information (PMI), which jointly masks a token n-gram if it exhibits high collocation over the corpus. PMI-Masking motivates, unifies, and improves upon prior more heuristic approaches that attempt to address the drawback of random uniform token masking, such as whole-word masking, entity/phrase masking, and random-span masking. Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of pretraining. | https://openreview.net/pdf/0ea2c13c0234ad53369c2787936620d2f84ce64d.pdf |
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize | https://openreview.net/forum?id=KUDUoRsEphu | https://openreview.net/forum?id=KUDUoRsEphu | Nils Wandel,Michael Weinmann,Reinhard Klein | ICLR 2021,Spotlight | Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer-generated imagery to computer-aided design in research and development. However, solving the partial differential equations of incompressible fluids is a challenging task and traditional numerical approximation schemes come at high computational costs. Recent deep learning based approaches promise vast speed-ups but do not generalize to new fluid domains, require fluid simulation data for training, or rely on complex pipelines that outsource major parts of the fluid simulation to traditional methods.
In this work, we propose a novel physics-constrained training approach that generalizes to new fluid domains, requires no fluid simulation data, and allows convolutional neural networks to map a fluid state from time-point t to a subsequent state at time t+dt in a single forward pass. This simplifies the pipeline to train and evaluate neural fluid models. After training, the framework yields models that are capable of fast fluid simulations and can handle various fluid phenomena including the Magnus effect and Kármán vortex streets. We present an interactive real-time demo to show the speed and generalization capabilities of our trained models. Moreover, the trained neural networks are efficient differentiable fluid solvers as they offer a differentiable update step to advance the fluid simulation in time. We exploit this fact in a proof-of-concept optimal control experiment. Our models significantly outperform a recent differentiable fluid solver in terms of computational speed and accuracy. | https://openreview.net/pdf/a304e46c25faf8b1991a7580669d779b3c3e2cd6.pdf |
A Gradient Flow Framework For Analyzing Network Pruning | https://openreview.net/forum?id=rumv7QmLUue | https://openreview.net/forum?id=rumv7QmLUue | Ekdeep Singh Lubana,Robert P. Dick | ICLR 2021,Spotlight | Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed to prune trained models. Despite lacking justification for their use early-on in training, such measures result in surprisingly low accuracy loss. To better explain this behavior, we develop a general framework that uses gradient flow to unify state-of-the-art importance measures through the norm of model parameters. We use this framework to determine the relationship between pruning measures and evolution of model parameters, establishing several results related to pruning models early-on in training: (i) magnitude-based pruning removes parameters that contribute least to reduction in loss, resulting in models that converge faster than magnitude-agnostic methods; (ii) loss-preservation based pruning preserves first-order model evolution dynamics and its use is therefore justified for pruning minimally trained models; and (iii) gradient-norm based pruning affects second-order model evolution dynamics, such that increasing gradient norm via pruning can produce poorly performing models. We validate our claims on several VGG-13, MobileNet-V1, and ResNet-56 models trained on CIFAR-10/CIFAR-100. | https://openreview.net/pdf/63800abe280bf68010c0dc83d2d4d40dc80e496a.pdf |
Towards Robustness Against Natural Language Word Substitutions | https://openreview.net/forum?id=ks5nebunVn_ | https://openreview.net/forum?id=ks5nebunVn_ | Xinshuai Dong,Anh Tuan Luu,Rongrong Ji,Hong Liu | ICLR 2021,Spotlight | Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing. Previous defense methods capture word substitutions in vector space by using either l_2-ball or hyper-rectangle, which results in perturbation sets that are not inclusive enough or unnecessarily large, and thus impedes mimicry of worst cases for robust training. In this paper, we introduce a novel Adversarial Sparse Convex Combination (ASCC) method. We model the word substitution attack space as a convex hull and leverages a regularization term to enforce perturbation towards an actual substitution, thus aligning our modeling better with the discrete textual space. Based on ASCC method, we further propose ASCC-defense, which leverages ASCC to generate worst-case perturbations and incorporates adversarial training towards robustness. Experiments show that ASCC-defense outperforms the current state-of-the-arts in terms of robustness on two prevailing NLP tasks, i.e., sentiment analysis and natural language inference, concerning several attacks across multiple model architectures. Besides, we also envision a new class of defense towards robustness in NLP, where our robustly trained word vectors can be plugged into a normally trained model and enforce its robustness without applying any other defense techniques. | https://openreview.net/pdf/164becb7cba519983d9f4cb5dfe5a1661e8cfa13.pdf |
Iterative Empirical Game Solving via Single Policy Best Response | https://openreview.net/forum?id=R4aWTjmrEKM | https://openreview.net/forum?id=R4aWTjmrEKM | Max Smith,Thomas Anthony,Michael Wellman | ICLR 2021,Spotlight | Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (DRL).
At each iteration, DRL is invoked to train a best response to a mixture of opponent policies.
The repeated application of DRL poses an expensive computational burden as we look to apply this algorithm to more complex domains.
We introduce two variations of PSRO designed to reduce the amount of simulation required during DRL training.
Both algorithms modify how PSRO adds new policies to the empirical game, based on learned responses to a single opponent policy.
The first, Mixed-Oracles, transfers knowledge from previous iterations of DRL, requiring training only against the opponent's newest policy.
The second, Mixed-Opponents, constructs a pure-strategy opponent by mixing existing strategy's action-value estimates, instead of their policies.
Learning against a single policy mitigates conflicting experiences on behalf of a learner facing an unobserved distribution of opponents.
We empirically demonstrate that these algorithms substantially reduce the amount of simulation during training required by PSRO, while producing equivalent or better solutions to the game. | https://openreview.net/pdf/fb60abf7fdce06cdc3bb6ee76b85965240d6427d.pdf |
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