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Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies | https://openreview.net/forum?id=JJCjv4dAbyL | https://openreview.net/forum?id=JJCjv4dAbyL | Alon Berliner,Guy Rotman,Yossi Adi,Roi Reichart,Tamir Hazan | ICLR 2022,Poster | Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through the relevant structures often requires enumerating over an exponentially large latent space. Recently, various approaches were devised to propagate approximated gradients without enumerating over the space of possible structures. In this work, we use Natural Evolution Strategies (NES), a class of gradient-free black-box optimization algorithms, to learn discrete structured VAEs. The NES algorithms are computationally appealing as they estimate gradients with forward pass evaluations only, thus they do not require to propagate gradients through their discrete structures. We demonstrate empirically that optimizing discrete structured VAEs using NES is as effective as gradient-based approximations. Lastly, we prove NES converges for non-Lipschitz functions as appear in discrete structured VAEs. | https://openreview.net/pdf/31efa202599273e261068a5a41dffffc4fe9a922.pdf |
Learning Features with Parameter-Free Layers | https://openreview.net/forum?id=bCrdi4iVvv | https://openreview.net/forum?id=bCrdi4iVvv | Dongyoon Han,YoungJoon Yoo,Beomyoung Kim,Byeongho Heo | ICLR 2022,Poster | Trainable layers such as convolutional building blocks are the standard network design choices by learning parameters to capture the global context through successive spatial operations. When designing an efficient network, trainable layers such as the depthwise convolution is the source of efficiency in the number of parameters and FLOPs, but there was little improvement to the model speed in practice. This paper argues that simple built-in parameter-free operations can be a favorable alternative to the efficient trainable layers replacing spatial operations in a network architecture. We aim to break the stereotype of organizing the spatial operations of building blocks into trainable layers. Extensive experimental analyses based on layer-level studies with fully-trained models and neural architecture searches are provided to investigate whether parameter-free operations such as the max-pool are functional. The studies eventually give us a simple yet effective idea for redesigning network architectures, where the parameter-free operations are heavily used as the main building block without sacrificing the model accuracy as much. Experimental results on the ImageNet dataset demonstrate that the network architectures with parameter-free operations could enjoy the advantages of further efficiency in terms of model speed, the number of the parameters, and FLOPs. Code and ImageNet pretrained models are available at https://github.com/naver-ai/PfLayer.
| https://openreview.net/pdf/45b77b67f9fd2463226093e385789c3f8167284d.pdf |
Denoising Likelihood Score Matching for Conditional Score-based Data Generation | https://openreview.net/forum?id=LcF-EEt8cCC | https://openreview.net/forum?id=LcF-EEt8cCC | Chen-Hao Chao,Wei-Fang Sun,Bo-Wun Cheng,Yi-Chen Lo,Chia-Che Chang,Yu-Lun Liu,Yu-Lin Chang,Chia-Ping Chen,Chun-Yi Lee | ICLR 2022,Poster | Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier. However, our analysis indicates that the training objectives for the classifier in these methods may lead to a serious score mismatch issue, which corresponds to the situation that the estimated scores deviate from the true ones. Such an issue causes the samples to be misled by the deviated scores during the diffusion process, resulting in a degraded sampling quality. To resolve it, we theoretically formulate a novel training objective, called Denoising Likelihood Score Matching (DLSM) loss, for the classifier to match the gradients of the true log likelihood density. Our experimental evidences show that the proposed method outperforms the previous methods on both Cifar-10 and Cifar-100 benchmarks noticeably in terms of several key evaluation metrics. We thus conclude that, by adopting DLSM, the conditional scores can be accurately modeled, and the effect of the score mismatch issue is alleviated. | https://openreview.net/pdf/ccc930d6cefd7ea8811e858a55d6318d0ea95abb.pdf |
Memory Replay with Data Compression for Continual Learning | https://openreview.net/forum?id=a7H7OucbWaU | https://openreview.net/forum?id=a7H7OucbWaU | Liyuan Wang,Xingxing Zhang,Kuo Yang,Longhui Yu,Chongxuan Li,Lanqing HONG,Shifeng Zhang,Zhenguo Li,Yi Zhong,Jun Zhu | ICLR 2022,Poster | Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance. However, existing work is mainly built on a small memory buffer containing a few original data, which cannot fully characterize the old data distribution. In this work, we propose memory replay with data compression to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer. Observing that the trade-off between the quality and quantity of compressed data is highly nontrivial for the efficacy of memory replay, we propose a novel method based on determinantal point processes (DPPs) to efficiently determine an appropriate compression quality for currently-arrived training samples. In this way, using a naive data compression algorithm with a properly selected quality can largely boost recent strong baselines by saving more compressed data in a limited storage space. We extensively validate this across several benchmarks of class-incremental learning and in a realistic scenario of object detection for autonomous driving. | https://openreview.net/pdf/f05469fd5cb6748c9852f8b46453a984b34fe0f6.pdf |
MAML is a Noisy Contrastive Learner in Classification | https://openreview.net/forum?id=LDAwu17QaJz | https://openreview.net/forum?id=LDAwu17QaJz | Chia Hsiang Kao,Wei-Chen Chiu,Pin-Yu Chen | ICLR 2022,Poster | Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems. Yet, with the unique design of nested inner-loop and outer-loop updates, which govern the task-specific and meta-model-centric learning, respectively, the underlying learning objective of MAML remains implicit, impeding a more straightforward understanding of it. In this paper, we provide a new perspective of the working mechanism of MAML. We discover that MAML is analogous to a meta-learner using a supervised contrastive objective in classification. The query features are pulled towards the support features of the same class and against those of different classes. Such contrastiveness is experimentally verified via an analysis based on the cosine similarity. Moreover, we reveal that vanilla MAML has an undesirable interference term originating from the random initialization and the cross-task interaction. We thus propose a simple but effective technique, the zeroing trick, to alleviate the interference. Extensive experiments are conducted on both mini-ImageNet and Omniglot datasets to validate the consistent improvement brought by our proposed method. | https://openreview.net/pdf/0139eec2530d6f2ebd6cb972ba3c98769ed552df.pdf |
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning | https://openreview.net/forum?id=afoV8W3-IYp | https://openreview.net/forum?id=afoV8W3-IYp | Xiaojian Ma,Weili Nie,Zhiding Yu,Huaizu Jiang,Chaowei Xiao,Yuke Zhu,Song-Chun Zhu,Anima Anandkumar | ICLR 2022,Poster | Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i.e., systematic generalization. In this work, we use vision transformers (ViTs) as our base model for visual reasoning and make better use of concepts defined as object entities and their relations to improve the reasoning ability of ViTs. Specifically, we introduce a novel concept-feature dictionary to allow flexible image feature retrieval at training time with concept keys. This dictionary enables two new concept-guided auxiliary tasks: 1) a global task for promoting relational reasoning, and 2) a local task for facilitating semantic object-centric correspondence learning. To examine the systematic generalization of visual reasoning models, we introduce systematic splits for the standard HICO and GQA benchmarks. We show the resulting model, Concept-guided Vision Transformer (or RelViT for short) significantly outperforms prior approaches on HICO and GQA by 16% and 13% in the original split, and by 43% and 18% in the systematic split. Our ablation analyses also reveal our model's compatibility with multiple ViT variants and robustness to hyper-parameters. | https://openreview.net/pdf/9ae93c86cdada9dfdeef3cf2c7ee77363fe51c7b.pdf |
Boosted Curriculum Reinforcement Learning | https://openreview.net/forum?id=anbBFlX1tJ1 | https://openreview.net/forum?id=anbBFlX1tJ1 | Pascal Klink,Carlo D'Eramo,Jan Peters,Joni Pajarinen | ICLR 2022,Poster | Curriculum value-based reinforcement learning (RL) solves a complex target task by reusing action-values across a tailored sequence of related tasks of increasing difficulty. However, finding an exact way of reusing action-values in this setting is still a poorly understood problem. In this paper, we introduce the concept of boosting to curriculum value-based RL, by approximating the action-value function as a sum of residuals trained on each task. This approach, which we refer to as boosted curriculum reinforcement learning (BCRL), has the benefit of naturally increasing the representativeness of the functional space by adding a new residual each time a new task is presented. This procedure allows reusing previous action-values while promoting expressiveness of the action-value function. We theoretically study BCRL as an approximate value iteration algorithm, discussing advantages over regular curriculum RL in terms of approximation accuracy and convergence to the optimal action-value function. Finally, we provide detailed empirical evidence of the benefits of BCRL in problems requiring curricula for accurate action-value estimation and targeted exploration. | https://openreview.net/pdf/066eed802f996fdef355f574e65bcaba5794933a.pdf |
ViDT: An Efficient and Effective Fully Transformer-based Object Detector | https://openreview.net/forum?id=w4cXZDDib1H | https://openreview.net/forum?id=w4cXZDDib1H | Hwanjun Song,Deqing Sun,Sanghyuk Chun,Varun Jampani,Dongyoon Han,Byeongho Heo,Wonjae Kim,Ming-Hsuan Yang | ICLR 2022,Poster | Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrate Vision and Detection Transformers (ViDT) to build an effective and efficient object detector. ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector, followed by a computationally efficient transformer decoder that exploits multi-scale features and auxiliary techniques essential to boost the detection performance without much increase in computational load. Extensive evaluation results on the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP and latency trade-off among existing fully transformer-based object detectors, and achieves 49.2AP owing to its high scalability for large models. We release the code and trained models at https://github.com/naver-ai/vidt. | https://openreview.net/pdf/264491cc60f75b87764768f41d002c6f4bee883e.pdf |
BiBERT: Accurate Fully Binarized BERT | https://openreview.net/forum?id=5xEgrl_5FAJ | https://openreview.net/forum?id=5xEgrl_5FAJ | Haotong Qin,Yifu Ding,Mingyuan Zhang,Qinghua YAN,Aishan Liu,Qingqing Dang,Ziwei Liu,Xianglong Liu | ICLR 2022,Poster | The large pre-trained BERT has achieved remarkable performance on Natural Language Processing (NLP) tasks but is also computation and memory expensive. As one of the powerful compression approaches, binarization extremely reduces the computation and memory consumption by utilizing 1-bit parameters and bitwise operations. Unfortunately, the full binarization of BERT (i.e., 1-bit weight, embedding, and activation) usually suffer a significant performance drop, and there is rare study addressing this problem. In this paper, with the theoretical justification and empirical analysis, we identify that the severe performance drop can be mainly attributed to the information degradation and optimization direction mismatch respectively in the forward and backward propagation, and propose BiBERT, an accurate fully binarized BERT, to eliminate the performance bottlenecks. Specifically, BiBERT introduces an efficient Bi-Attention structure for maximizing representation information statistically and a Direction-Matching Distillation (DMD) scheme to optimize the full binarized BERT accurately. Extensive experiments show that BiBERT outperforms both the straightforward baseline and existing state-of-the-art quantized BERTs with ultra-low bit activations by convincing margins on the NLP benchmark. As the first fully binarized BERT, our method yields impressive 56.3 times and 31.2 times saving on FLOPs and model size, demonstrating the vast advantages and potential of the fully binarized BERT model in real-world resource-constrained scenarios. | https://openreview.net/pdf/09aef2ecce1fcaf41eaa870ad5afc7e4d3222dad.pdf |
Feature Kernel Distillation | https://openreview.net/forum?id=tBIQEvApZK5 | https://openreview.net/forum?id=tBIQEvApZK5 | Bobby He,Mete Ozay | ICLR 2022,Poster | Trained Neural Networks (NNs) can be viewed as data-dependent kernel machines, with predictions determined by the inner product of last-layer representations across inputs, referred to as the feature kernel. We explore the relevance of the feature kernel for Knowledge Distillation (KD), using a mechanistic understanding of an NN’s optimisation process. We extend the theoretical analysis of Allen-Zhu & Li (2020) to show that a trained NN’s feature kernel is highly dependent on its parameter initialisation, which biases different initialisations of the same architecture to learn different data attributes in a multi-view data setting. This enables us to prove that KD using only pairwise feature kernel comparisons can improve NN test accuracy in such settings, with both single & ensemble teacher models, whereas standard training without KD fails to generalise. We further use our theory to motivate practical considerations for improving student generalisation when using distillation with feature kernels, which allows us to propose a novel approach: Feature Kernel Distillation (FKD). Finally, we experimentally corroborate our theory in the image classification setting, showing that FKD is amenable to ensemble distillation, can transfer knowledge across datasets, and outperforms both vanilla KD & other feature kernel based KD baselines across a range of standard architectures & datasets. | https://openreview.net/pdf/60c40aa9a9f7d00701fc1025d8f2d4245ad5ced2.pdf |
Representation-Agnostic Shape Fields | https://openreview.net/forum?id=-ngwPqanCEZ | https://openreview.net/forum?id=-ngwPqanCEZ | Xiaoyang Huang,Jiancheng Yang,Yanjun Wang,Ziyu Chen,Linguo Li,Teng Li,Bingbing Ni,Wenjun Zhang | ICLR 2022,Poster | 3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, PointNet for point clouds and VoxNet for voxels. In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning. RASF is implemented with a learnable 3D grid with multiple channels to store local geometry. Based on RASF, shape embeddings for various 3D shape representations (point clouds, meshes and voxels) are retrieved by coordinate indexing. While there are multiple ways to optimize the learnable parameters of RASF, we provide two effective schemes among all in this paper for RASF pre-training: shape reconstruction and normal estimation. Once trained, RASF becomes a plug-and-play performance booster with negligible cost. Extensive experiments on diverse 3D representation formats, networks and applications, validate the universal effectiveness of the proposed RASF. Code and pre-trained models are publicly available\footnote{\url{https://github.com/seanywang0408/RASF}}. | https://openreview.net/pdf/3dd6d2a26afbbd24597f42af0a520b3b487fc536.pdf |
Learning Synthetic Environments and Reward Networks for Reinforcement Learning | https://openreview.net/forum?id=C1_esHN6AVn | https://openreview.net/forum?id=C1_esHN6AVn | Fabio Ferreira,Thomas Nierhoff,Andreas Sälinger,Frank Hutter | ICLR 2022,Poster | We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by learning about its state dynamics and rewards, an RN is a partial proxy that learns to augment or replace rewards. We use bi-level optimization to evolve SEs and RNs: the inner loop trains the RL agent, and the outer loop trains the parameters of the SE / RN via an evolution strategy. We evaluate our proposed new concept on a broad range of RL algorithms and classic control environments. In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment. However, once such an SE has been learned, we do not need any interactions with the real environment to train new agents. Moreover, the learned SE proxies allow us to train agents with fewer interactions while maintaining the original task performance. Our empirical results suggest that SEs achieve this result by learning informed representations that bias the agents towards relevant states. Moreover, we find that these proxies are robust against hyperparameter variation and can also transfer to unseen agents. | https://openreview.net/pdf/706b0f26d6790fba3e56e1ae94751cce8f4a0789.pdf |
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning | https://openreview.net/forum?id=NH29920YEmj | https://openreview.net/forum?id=NH29920YEmj | Changchun Li,Ximing Li,Lei Feng,Jihong Ouyang | ICLR 2022,Poster | Positive and Unlabeled (PU) learning targets inducing a binary classifier from weak training datasets of positive and unlabeled instances, which arise in many real-world applications. In this paper, we propose a novel PU learning method, namely Positive and unlabeled learning with Partially Positive Mixup (P3Mix), which simultaneously benefits from data augmentation and supervision correction with a heuristic mixup technique. To be specific, we take inspiration from the directional boundary deviation phenomenon observed in our preliminary experiments, where the learned PU boundary tends to deviate from the fully supervised boundary towards the positive side. For the unlabeled instances with ambiguous predictive results, we select their mixup partners from the positive instances around the learned PU boundary, so as to transform them into augmented instances near to the boundary yet with more precise supervision. Accordingly, those augmented instances may push the learned PU boundary towards the fully supervised boundary, thereby improving the classification performance. Comprehensive experimental results demonstrate the effectiveness of the heuristic mixup technique in PU learning and show that P3Mix can consistently outperform the state-of-the-art PU learning methods. | https://openreview.net/pdf/d687929225415551919e783b0c52f61382054101.pdf |
Incremental False Negative Detection for Contrastive Learning | https://openreview.net/forum?id=dDjSKKA5TP1 | https://openreview.net/forum?id=dDjSKKA5TP1 | Tsai-Shien Chen,Wei-Chih Hung,Hung-Yu Tseng,Shao-Yi Chien,Ming-Hsuan Yang | ICLR 2022,Poster | Self-supervised learning has recently shown great potential in vision tasks through contrastive learning, which aims to discriminate each image, or instance, in the dataset. However, such instance-level learning ignores the semantic relationship among instances and sometimes undesirably repels the anchor from the semantically similar samples, termed as "false negatives". In this work, we show that the unfavorable effect from false negatives is more significant for the large-scale datasets with more semantic concepts. To address the issue, we propose a novel self-supervised contrastive learning framework that incrementally detects and explicitly removes the false negative samples. Specifically, following the training process, our method dynamically detects increasing high-quality false negatives considering that the encoder gradually improves and the embedding space becomes more semantically structural. Next, we discuss two strategies to explicitly remove the detected false negatives during contrastive learning. Extensive experiments show that our framework outperforms other self-supervised contrastive learning methods on multiple benchmarks in a limited resource setup. | https://openreview.net/pdf/bb320db0548af9f9dda16d602425fa7632befbb0.pdf |
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style | https://openreview.net/forum?id=rJvY_5OzoI | https://openreview.net/forum?id=rJvY_5OzoI | Siddharth Mysore,George Cheng,Yunqi Zhao,Kate Saenko,Meng Wu | ICLR 2022,Poster | Using a single value function (critic) shared over multiple tasks in Actor-Critic multi-task reinforcement learning (MTRL) can result in negative interference between tasks, which can compromise learning performance. Multi-Critic Actor Learning (MultiCriticAL) proposes instead maintaining separate critics for each task being trained while training a single multi-task actor. Explicitly distinguishing between tasks also eliminates the need for critics to learn to do so and mitigates interference between task-value estimates. MultiCriticAL is tested in the context of multi-style learning, a special case of MTRL where agents are trained to behave with different distinct behavior styles, and yields up to 56% performance gains over the single-critic baselines and even successfully learns behavior styles in cases where single-critic approaches may simply fail to learn. In a simulated real-world use case, MultiCriticAL enables learning policies that smoothly transition between multiple fighting styles on an experimental build of EA’s UFC game. | https://openreview.net/pdf/9ba47239c3de56ba46f2cac821cf17c1791a8444.pdf |
Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring | https://openreview.net/forum?id=kezNJydWvE | https://openreview.net/forum?id=kezNJydWvE | Seungjun Nah,Sanghyun Son,Jaerin Lee,Kyoung Mu Lee | ICLR 2022,Poster | The goal of dynamic scene deblurring is to remove the motion blur in a given image. Typical learning-based approaches implement their solutions by minimizing the L1 or L2 distance between the output and the reference sharp image. Recent attempts adopt visual recognition features in training to improve the perceptual quality. However, those features are primarily designed to capture high-level contexts rather than low-level structures such as blurriness. Instead, we propose a more direct way to make images sharper by exploiting the inverse task of deblurring, namely, reblurring. Reblurring amplifies the remaining blur to rebuild the original blur, however, a well-deblurred clean image with zero-magnitude blur is hard to reblur. Thus, we design two types of reblurring loss functions for better deblurring. The supervised reblurring loss at training stage compares the amplified blur between the deblurred and the sharp images. The self-supervised reblurring loss at inference stage inspects if noticeable blur remains in the deblurred. Our experimental results on large-scale benchmarks and real images demonstrate the effectiveness of the reblurring losses in improving the perceptual quality of the deblurred images in terms of NIQE and LPIPS scores as well as visual sharpness. | https://openreview.net/pdf/1636f146bd4ebc888881dafbf13129a2072ba805.pdf |
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View | https://openreview.net/forum?id=j-63FSNcO5a | https://openreview.net/forum?id=j-63FSNcO5a | Xuanchi Ren,Tao Yang,Yuwang Wang,Wenjun Zeng | ICLR 2022,Poster | From the intuitive notion of disentanglement, the image variations corresponding to different generative factors should be distinct from each other, and the disentangled representation should reflect those variations with separate dimensions. To discover the generative factors and learn disentangled representation, previous methods typically leverage an extra regularization term when learning to generate realistic images. However, the term usually results in a trade-off between disentanglement and generation quality. For the generative models pretrained without any disentanglement term, the generated images show semantically meaningful variations when traversing along different directions in the latent space. Based on this observation, we argue that it is possible to mitigate the trade-off by (i) leveraging the pretrained generative models with high generation quality, (ii) focusing on discovering the traversal directions as generative factors for disentangled representation learning. To achieve this, we propose Disentaglement via Contrast (DisCo) as a framework to model the variations based on the target disentangled representations, and contrast the variations to jointly discover disentangled directions and learn disentangled representations. DisCo achieves the state-of-the-art disentangled representation learning and distinct direction discovering, given pretrained non-disentangled generative models including GAN, VAE, and Flow. Source code is at https://github.com/xrenaa/DisCo. | https://openreview.net/pdf/e69c1d9f39564cc023d156cc675d4675ad2da583.pdf |
Towards Building A Group-based Unsupervised Representation Disentanglement Framework | https://openreview.net/forum?id=YgPqNctmyd | https://openreview.net/forum?id=YgPqNctmyd | Tao Yang,Xuanchi Ren,Yuwang Wang,Wenjun Zeng,Nanning Zheng | ICLR 2022,Poster | Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. The key idea of the state-of-the-art VAE-based unsupervised representation disentanglement methods is to minimize the total correlation of the joint distribution of the latent variables. However, it has been proved that their goal can not be achieved without introducing other inductive biases. The Group Theory based definition of representation disentanglement mathematically connects the data transformations to the representations using the formalism of group. In this paper, built on the group-based definition and inspired by the \emph{n-th dihedral group}, we first propose a theoretical framework towards achieving unsupervised representation disentanglement. We then propose a model based on existing VAE-based methods to tackle the unsupervised learning problem of the framework. In the theoretical framework, we prove three sufficient conditions on model, group structure, and data respectively in an effort to achieve, in an unsupervised way, disentangled representation per group-based definition. With these conditions, we offer an option, from the perspective of the group-based definition, for the inductive bias that existing VAE-based models lack. Experimentally, we train 1800 models covering the most prominent VAE-based methods on five datasets to verify the effectiveness of our theoretical framework. Compared to the original VAE-based methods, these Groupified VAEs consistently achieve better mean performance with smaller variances. | https://openreview.net/pdf/5394844d0eb50251cdb0af3ec98822f39236eb03.pdf |
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning | https://openreview.net/forum?id=AjGC97Aofee | https://openreview.net/forum?id=AjGC97Aofee | Yulun Zhang,Huan Wang,Can Qin,Yun Fu | ICLR 2022,Poster | Several image super-resolution (SR) networks have been proposed of late for efficient SR, achieving promising results. However, they are still not lightweight enough and neglect to be extended to larger networks. At the same time, model compression techniques, like neural architecture search and knowledge distillation, typically consume considerable computation resources. In contrast, network pruning is a cheap and effective model compression technique. However, it is hard to be applied to SR networks directly because filter pruning for residual blocks is well-known tricky. To address the above issues, we propose structure-regularized pruning (SRP), which imposes regularization on the pruned structure to ensure the locations of pruned filters are aligned across different layers. Specifically, for the layers connected by the same residual, we select the filters of the same indices as unimportant filters. To transfer the expressive power in the unimportant filters to the rest of the network, we employ $L_2$ regularization to drive the weights towards zero so that eventually, their absence will cause minimal performance degradation. We apply SRP to train efficient image SR networks, resulting in a lightweight network SRPN-Lite and a very deep one SRPN. We conduct extensive comparisons with both lightweight and larger networks. SRPN-Lite and SRPN perform favorably against other recent efficient SR approaches quantitatively and visually. | https://openreview.net/pdf/538af917ae93b620773d2ce7ce65f827922d0e3f.pdf |
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | https://openreview.net/forum?id=g1SzIRLQXMM | https://openreview.net/forum?id=g1SzIRLQXMM | Franziska Geiger,Martin Schrimpf,Tiago Marques,James J. DiCarlo | ICLR 2022,Spotlight | After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are considered poor models of the development of the visual system because they posit millions of sequential, precisely coordinated synaptic updates, each based on a labeled image. While ongoing research is pursuing the use of unsupervised proxies for labels, we here explore a complementary strategy of reducing the required number of supervised synaptic updates to produce an adult-like ventral visual stream (as judged by the match to V1, V2, V4, IT, and behavior). Such models might require less precise machinery and energy expenditure to coordinate these updates and would thus move us closer to viable neuroscientific hypotheses about how the visual system wires itself up. Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies: First, we find that only 2% of supervised updates (epochs and images) are needed to achieve 80% of the match to adult ventral stream. Specifically, training benefits predictions of higher visual cortex the most whereas early visual cortex predictions only improve marginally over the course of training. Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved “at birth" (i.e. no training at all). Third, we find that, by training only 5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream. This approach further improves on ImageNet performance over previous attempts in computer vision of minimizing trained components without substantially increasing the relative number of trained parameters. These results reflect first steps in modeling not just primate adult visual processing during inference, but also how the ventral visual stream might be "wired up" by evolution (a model's "birth" state) and by developmental learning (a model's updates based on visual experience). | https://openreview.net/pdf/52d6dfe1e693892f0382d80002c972022ced3a49.pdf |
Dynamics-Aware Comparison of Learned Reward Functions | https://openreview.net/forum?id=CALFyKVs87 | https://openreview.net/forum?id=CALFyKVs87 | Blake Wulfe,Logan Michael Ellis,Jean Mercat,Rowan Thomas McAllister,Adrien Gaidon | ICLR 2022,Spotlight | The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, $\textit{comparing}$ reward functions, for example as a means of evaluating reward learning methods, presents a challenge. Reward functions are typically compared by considering the behavior of optimized policies, but this approach conflates deficiencies in the reward function with those of the policy search algorithm used to optimize it. To address this challenge, Gleave et al. (2020) propose the Equivalent-Policy Invariant Comparison (EPIC) distance. EPIC avoids policy optimization, but in doing so requires computing reward values at transitions that may be impossible under the system dynamics. This is problematic for learned reward functions because it entails evaluating them outside of their training distribution, resulting in inaccurate reward values that we show can render EPIC ineffective at comparing rewards. To address this problem, we propose the Dynamics-Aware Reward Distance (DARD), a new reward pseudometric. DARD uses an approximate transition model of the environment to transform reward functions into a form that allows for comparisons that are invariant to reward shaping while only evaluating reward functions on transitions close to their training distribution. Experiments in simulated physical domains demonstrate that DARD enables reliable reward comparisons without policy optimization and is significantly more predictive than baseline methods of downstream policy performance when dealing with learned reward functions. | https://openreview.net/pdf/14a7ecb3498b71a8fba347a8d3438e054084f561.pdf |
Learning Hierarchical Structures with Differentiable Nondeterministic Stacks | https://openreview.net/forum?id=5LXw_QplBiF | https://openreview.net/forum?id=5LXw_QplBiF | Brian DuSell,David Chiang | ICLR 2022,Spotlight | Learning hierarchical structures in sequential data -- from simple algorithmic patterns to natural language -- in a reliable, generalizable way remains a challenging problem for neural language models. Past work has shown that recurrent neural networks (RNNs) struggle to generalize on held-out algorithmic or syntactic patterns without supervision or some inductive bias. To remedy this, many papers have explored augmenting RNNs with various differentiable stacks, by analogy with finite automata and pushdown automata (PDAs). In this paper, we improve the performance of our recently proposed Nondeterministic Stack RNN (NS-RNN), which uses a differentiable data structure that simulates a nondeterministic PDA, with two important changes. First, the model now assigns unnormalized positive weights instead of probabilities to stack actions, and we provide an analysis of why this improves training. Second, the model can directly observe the state of the underlying PDA. Our model achieves lower cross-entropy than all previous stack RNNs on five context-free language modeling tasks (within 0.05 nats of the information-theoretic lower bound), including a task on which the NS-RNN previously failed to outperform a deterministic stack RNN baseline. Finally, we propose a restricted version of the NS-RNN that incrementally processes infinitely long sequences, and we present language modeling results on the Penn Treebank. | https://openreview.net/pdf/bfc2ff0a81fd70d01a09a0cb018dddf36e401060.pdf |
Sampling with Mirrored Stein Operators | https://openreview.net/forum?id=eMudnJsb1T5 | https://openreview.net/forum?id=eMudnJsb1T5 | Jiaxin Shi,Chang Liu,Lester Mackey | ICLR 2022,Spotlight | We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries. Stein Variational Mirror Descent and Mirrored Stein Variational Gradient Descent minimize the Kullback-Leibler (KL) divergence to constrained target distributions by evolving particles in a dual space defined by a mirror map. Stein Variational Natural Gradient exploits non-Euclidean geometry to more efficiently minimize the KL divergence to unconstrained targets. We derive these samplers from a new class of mirrored Stein operators and adaptive kernels developed in this work. We demonstrate that these new samplers yield accurate approximations to distributions on the simplex, deliver valid confidence intervals in post-selection inference, and converge more rapidly than prior methods in large-scale unconstrained posterior inference. Finally, we establish the convergence of our new procedures under verifiable conditions on the target distribution. | https://openreview.net/pdf/5c867e64ede248dde6dfa23bae9c1f0365022fdb.pdf |
Planning in Stochastic Environments with a Learned Model | https://openreview.net/forum?id=X6D9bAHhBQ1 | https://openreview.net/forum?id=X6D9bAHhBQ1 | Ioannis Antonoglou,Julian Schrittwieser,Sherjil Ozair,Thomas K Hubert,David Silver | ICLR 2022,Spotlight | Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is exemplified by MuZero, which has achieved state-of-the-art performance in a wide range of domains, from board games to visually rich environments, with discrete and continuous action spaces, in online and offline settings. However, previous instantiations of this approach were limited to the use of deterministic models. This limits their performance in environments that are inherently stochastic, partially observed, or so large and complex that they appear stochastic to a finite agent. In this paper we extend this approach to learn and plan with stochastic models. Specifically, we introduce a new algorithm, Stochastic MuZero, that learns a stochastic model incorporating afterstates, and uses this model to perform a stochastic tree search. Stochastic MuZero matched or exceeded the state of the art in a set of canonical single and multi-agent environments, including 2048 and backgammon, while maintaining the same performance as standard MuZero in the game of Go. | https://openreview.net/pdf/f49fc80947707469997960f573102cea38cafb0f.pdf |
RotoGrad: Gradient Homogenization in Multitask Learning | https://openreview.net/forum?id=T8wHz4rnuGL | https://openreview.net/forum?id=T8wHz4rnuGL | Adrián Javaloy,Isabel Valera | ICLR 2022,Spotlight | Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negative transfer. Previous works have tracked down this issue to the disparities in gradient magnitudes and directions across tasks, when optimizing the shared network parameters. While recent work has acknowledged that negative transfer is a two-fold problem, existing approaches fall short as they only focus on either homogenizing the gradient magnitude across tasks; or greedily change the gradient directions, overlooking future conflicts. In this work, we introduce RotoGrad, an algorithm that tackles negative transfer as a whole: it jointly homogenizes gradient magnitudes and directions, while ensuring training convergence. We show that RotoGrad outperforms competing methods in complex problems, including multi-label classification in CelebA and computer vision tasks in the NYUv2 dataset. A Pytorch implementation can be found in https://github.com/adrianjav/rotograd. | https://openreview.net/pdf/288f1ffa5c44be1b70664610932a5019dd24b6a1.pdf |
On Improving Adversarial Transferability of Vision Transformers | https://openreview.net/forum?id=D6nH3719vZy | https://openreview.net/forum?id=D6nH3719vZy | Muzammal Naseer,Kanchana Ranasinghe,Salman Khan,Fahad Khan,Fatih Porikli | ICLR 2022,Spotlight | Vision transformers (ViTs) process input images as sequences of patches via self-attention; a radically different architecture than convolutional neural networks (CNNs). This makes it interesting to study the adversarial feature space of ViT models and their transferability. In particular, we observe that adversarial patterns found via conventional adversarial attacks show very \emph{low} black-box transferability even for large ViT models. We show that this phenomenon is only due to the sub-optimal attack procedures that do not leverage the true representation potential of ViTs. A deep ViT is composed of multiple blocks, with a consistent architecture comprising of self-attention and feed-forward layers, where each block is capable of independently producing a class token. Formulating an attack using only the last class token (conventional approach) does not directly leverage the discriminative information stored in the earlier tokens, leading to poor adversarial transferability of ViTs.Using the compositional nature of ViT models, we enhance transferability of existing attacks by introducing two novel strategies specific to the architecture of ViT models. \emph{(i) Self-Ensemble:} We propose a method to find multiple discriminative pathways by dissecting a single ViT model into an ensemble of networks. This allows explicitly utilizing class-specific information at each ViT block. \emph{(ii) Token Refinement:} We then propose to refine the tokens to further enhance the discriminative capacity at each block of ViT.Our token refinement systematically combines the class tokens with structural information preserved within the patch tokens. An adversarial attack when applied to such refined tokens within the ensemble of classifiers found in a single vision transformer has significantly higher transferability and thereby brings out the true generalization potential of the ViT's adversarial space. Code: https://t.ly/hBbW. | https://openreview.net/pdf/b40e4df19f3e58593a885adc8809af1ba9864da8.pdf |
On Predicting Generalization using GANs | https://openreview.net/forum?id=eW5R4Cek6y6 | https://openreview.net/forum?id=eW5R4Cek6y6 | Yi Zhang,Arushi Gupta,Nikunj Saunshi,Sanjeev Arora | ICLR 2022,Spotlight | Research on generalization bounds for deep networks seeks to give ways to predict test error using just the training dataset and the network parameters. While generalization bounds can give many insights about architecture design, training algorithms etc., what they do not currently do is yield good predictions for actual test error. A recently introduced Predicting Generalization in Deep Learning competition aims to encourage discovery of methods to better predict test error. The current paper investigates a simple idea: can test error be predicted using {\em synthetic data,} produced using a Generative Adversarial Network (GAN) that was trained on the same training dataset? Upon investigating several GAN models and architectures, we find that this turns out to be the case.
In fact, using GANs pre-trained on standard datasets, the test error can be predicted without requiring any additional hyper-parameter tuning. This result is surprising because GANs have well-known limitations (e.g. mode collapse) and are known to not learn the data distribution accurately. Yet the generated samples are good enough to substitute for test data. Several additional experiments are presented to explore reasons why GANs do well at this task. In addition to a new approach for predicting generalization, the counter-intuitive phenomena presented in our work may also call for a better understanding of GANs' strengths and limitations. | https://openreview.net/pdf/c02dd2fa195251e1b7cc85379208fea1bc5f6a53.pdf |
On the Connection between Local Attention and Dynamic Depth-wise Convolution | https://openreview.net/forum?id=L3_SsSNMmy | https://openreview.net/forum?id=L3_SsSNMmy | Qi Han,Zejia Fan,Qi Dai,Lei Sun,Ming-Ming Cheng,Jiaying Liu,Jingdong Wang | ICLR 2022,Spotlight | Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in Local Vision Transformer, local attention, performs the attention separately over small local windows. We rephrase local attention as a channel-wise locally-connected layer and analyze it from two network regularization manners, sparse connectivity and weight sharing, as well as dynamic weight computation. We point out that local attention resembles depth-wise convolution and its dynamic variants in sparse connectivity: there is no connection across channels, and each position is connected to the positions within a small local window. The main differences lie in (i) weight sharing - depth-wise convolution shares connection weights (kernel weights) across spatial positions and attention shares the connection weights across channels, and (ii) dynamic weight computation manners - local attention is based on dot-products between pairwise positions in the local window, and dynamic convolution is based on linear projections conducted on the center representation or the globally pooled representation. The connection between local attention and dynamic depth-wise convolution is empirically verified by the ablation study about weight sharing and dynamic weight computation in Local Vision Transformer and (dynamic) depth-wise convolution. We empirically observe that the models based on depth-wise convolution and the dynamic variants with lower computation complexity perform on-par with or slightly better than Swin Transformer, an instance of Local Vision Transformer, for ImageNet classification, COCO object detection and ADE semantic segmentation. Code is available at https://github.com/Atten4Vis/DemystifyLocalViT. | https://openreview.net/pdf/b5b230d05deb5ca8dcfd87f952bca5621cf5cced.pdf |
Strength of Minibatch Noise in SGD | https://openreview.net/forum?id=uorVGbWV5sw | https://openreview.net/forum?id=uorVGbWV5sw | Liu Ziyin,Kangqiao Liu,Takashi Mori,Masahito Ueda | ICLR 2022,Spotlight | The noise in stochastic gradient descent (SGD), caused by minibatch sampling, is poorly understood despite its practical importance in deep learning. This work presents the first systematic study of the SGD noise and fluctuations close to a local minimum. We first analyze the SGD noise in linear regression in detail and then derive a general formula for approximating SGD noise in different types of minima. For application, our results (1) provide insight into the stability of training a neural network, (2) suggest that a large learning rate can help generalization by introducing an implicit regularization, (3) explain why the linear learning rate-batchsize scaling law fails at a large learning rate or at a small batchsize and (4) can provide an understanding of how discrete-time nature of SGD affects the recently discovered power-law phenomenon of SGD. | https://openreview.net/pdf/d9f2b7e88fec2d057ac6f54cbbd1ecdff0f06afb.pdf |
Learning more skills through optimistic exploration | https://openreview.net/forum?id=cU8rknuhxc | https://openreview.net/forum?id=cU8rknuhxc | DJ Strouse,Kate Baumli,David Warde-Farley,Volodymyr Mnih,Steven Stenberg Hansen | ICLR 2022,Spotlight | Unsupervised skill learning objectives (Eysenbach et al., 2019; Gregor et al., 2016) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate distinguishability by trying to infer latents from trajectories. The hope is for the agent to explore and master the environment by encouraging each skill (latent) to reliably reach different states. However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective. To combat this inherent pessimism towards exploration, we derive an information gain auxiliary objective that involves training an ensemble of discriminators and rewarding the policy for their disagreement. Our objective directly estimates the epistemic uncertainty that comes from the discriminator not having seen enough training examples, thus providing an intrinsic reward more tailored to the true objective compared to pseudocount-based methods (Burda et al., 2019). We call this exploration bonus discriminator disagreement intrinsic reward, or DISDAIN. We demonstrate empirically that DISDAIN improves skill learning both in a tabular grid world (Four Rooms) and the 57 games of the Atari Suite (from pixels). Thus, we encourage researchers to treat pessimism with DISDAIN. | https://openreview.net/pdf/fd77361933d33f5982e69d08631cf6222a3c48ce.pdf |
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory | https://openreview.net/forum?id=PLDOnFoVm4 | https://openreview.net/forum?id=PLDOnFoVm4 | Zhi Zhang,Zhuoran Yang,Han Liu,Pratap Tokekar,Furong Huang | ICLR 2022,Spotlight | We study reinforcement learning for partially observable multi-agent systems where each agent only has access to its own observation and reward and aims to maximize its cumulative rewards. To handle partial observations, we propose graph-assisted predictive state representations (GAPSR), a scalable multi-agent representation learning framework that leverages the agent connectivity graphs to aggregate local representations computed by each agent. In addition, our representations are readily able to incorporate dynamic interaction graphs and kernel space embeddings of the predictive states, and thus have strong flexibility and representation power.
Based on GAPSR, we propose an end-to-end MARL algorithm that simultaneously infers the predictive representations and uses the representations as the input of a policy optimization algorithm. Empirically, we demonstrate the efficacy of the proposed algorithm provided on both a MAMuJoCo robotic learning experiment and a multi-agent particle learning environment. | https://openreview.net/pdf/abd7a0683441b4eb75fb4381e8ac583f2bff2b90.pdf |
Adversarial Support Alignment | https://openreview.net/forum?id=26gKg6x-ie | https://openreview.net/forum?id=26gKg6x-ie | Shangyuan Tong,Timur Garipov,Yang Zhang,Shiyu Chang,Tommi S. Jaakkola | ICLR 2022,Spotlight | We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch between supports. We show that select discriminators (e.g. discriminator trained for Jensen-Shannon divergence) are able to map support differences as support differences in their one-dimensional output space. Following this result, our method aligns supports by minimizing a symmetrized relaxed optimal transport cost in the discriminator 1D space via an adversarial process. Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance. We quantitatively evaluate the method across domain adaptation tasks with shifts in label distributions. Our experiments show that the proposed method is more robust against these shifts than other alignment-based baselines. | https://openreview.net/pdf/baf81164820438550e81b120efdf1f7f96cd349d.pdf |
GreaseLM: Graph REASoning Enhanced Language Models | https://openreview.net/forum?id=41e9o6cQPj | https://openreview.net/forum?id=41e9o6cQPj | Xikun Zhang,Antoine Bosselut,Michihiro Yasunaga,Hongyu Ren,Percy Liang,Christopher D Manning,Jure Leskovec | ICLR 2022,Spotlight | Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasoning. While knowledge graphs (KG) are often used to augment LMs with structured representations of world knowledge, it remains an open question how to effectively fuse and reason over the KG representations and the language context, which provides situational constraints and nuances. In this work, we propose GreaseLM, a new model that fuses encoded representations from pretrained LMs and graph neural networks over multiple layers of modality interaction operations. Information from both modalities propagates to the other, allowing language context representations to be grounded by structured world knowledge, and allowing linguistic nuances (e.g., negation, hedging) in the context to inform the graph representations of knowledge. Our results on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMLE) domains demonstrate that GreaseLM can more reliably answer questions that require reasoning over both situational constraints and structured knowledge, even outperforming models 8x larger. | https://openreview.net/pdf/1a023786aa33b14412cd0596ee9247b562f4f4fe.pdf |
Learning meta-features for AutoML | https://openreview.net/forum?id=DTkEfj0Ygb8 | https://openreview.net/forum?id=DTkEfj0Ygb8 | Herilalaina Rakotoarison,Louisot Milijaona,Andry RASOANAIVO,Michele Sebag,Marc Schoenauer | ICLR 2022,Spotlight | This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed \mf s with the space of distributions on the hyper-parameter configurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to define a distribution of promising hyper-parameter configurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, AutoSklearn (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r.t. a given ML algorithm or pipeline. The source code is available at https://github.com/luxusg1/metabu. | https://openreview.net/pdf/2c85e44817e8f27a5434ef26f8089b6dbaec6dab.pdf |
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction | https://openreview.net/forum?id=Dup_dDqkZC5 | https://openreview.net/forum?id=Dup_dDqkZC5 | Roger Girgis,Florian Golemo,Felipe Codevilla,Martin Weiss,Jim Aldon D'Souza,Samira Ebrahimi Kahou,Felix Heide,Christopher Pal | ICLR 2022,Spotlight | Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transformers which are encoder-decoder architectures that generate scene-consistent multi-agent trajectories. We refer to these architectures as “AutoBots”. The encoder is a stack of interleaved temporal and social multi-head self-attention (MHSA) modules which alternately perform equivariant processing across the temporal and social dimensions. The decoder employs learnable seed parameters in combination with temporal and social MHSA modules allowing it to perform inference over the
entire future scene in a single forward pass efficiently. AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene. For the single-agent prediction case, our model achieves top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results on the Argoverse vehicle prediction challenge. In the multi-agent setting, we evaluate on the synthetic partition of TrajNet++ dataset to showcase the model’s socially-consistent predictions. We also demonstrate our model on general sequences of sets and provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. A distinguishing feature of AutoBots is that all models are trainable on a
single desktop GPU (1080 Ti) in under 48h. | https://openreview.net/pdf/1ab1260f39e79ac98b52759c8221374f595af7aa.pdf |
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective | https://openreview.net/forum?id=5FUq05QRc5b | https://openreview.net/forum?id=5FUq05QRc5b | Qi Lyu,Xiao Fu,Weiran Wang,Songtao Lu | ICLR 2022,Spotlight | Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA], while the artificial ones are frequently used in self-supervised learning (SSL) paradigms, e.g., BYOL and Barlow Twins. Both types of approaches often involve learning neural feature extractors such that the embeddings of data exhibit high cross-view correlations. Although intuitive, the effectiveness of correlation-based neural embedding is mostly empirically validated.
This work aims to understand latent correlation maximization-based deep multiview learning from a latent component identification viewpoint. An intuitive generative model of multiview data is adopted, where the views are different nonlinear mixtures of shared and private components. Since the shared components are view/distortion-invariant, representing the data using such components is believed to reveal the identity of the samples effectively and robustly. Under this model, latent correlation maximization is shown to guarantee the extraction of the shared components across views (up to certain ambiguities). In addition, it is further shown that the private information in each view can be provably disentangled from the shared using proper regularization design. A finite sample analysis, which has been rare in nonlinear mixture identifiability study, is also presented. The theoretical results and newly designed regularization are tested on a series of tasks. | https://openreview.net/pdf/a4489765925696bd54b9091ab12640cebd4b76ce.pdf |
Deconstructing the Inductive Biases of Hamiltonian Neural Networks | https://openreview.net/forum?id=EDeVYpT42oS | https://openreview.net/forum?id=EDeVYpT42oS | Nate Gruver,Marc Anton Finzi,Samuel Don Stanton,Andrew Gordon Wilson | ICLR 2022,Spotlight | Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These models, however, are challenging to apply to many real world systems, such as those that don’t conserve energy or contain contacts, a common setting for robotics and reinforcement learning. In this paper, we examine the inductive biases that make physics-inspired models successful in practice. We show that, contrary to conventional wisdom, the improved generalization of HNNs is the result of modeling acceleration directly and avoiding artificial complexity from the coordinate system, rather than symplectic structure or energy conservation. We show that by relaxing the inductive biases of these models, we can match or exceed performance on energy-conserving systems while dramatically improving performance on practical, non-conservative systems. We extend this approach to constructing transition models for common Mujoco environments, showing that our model can appropriately balance inductive biases with the flexibility required for model-based control. | https://openreview.net/pdf/0c5f008200cc427b5b6c416a2ec60e6adf6df996.pdf |
Memorizing Transformers | https://openreview.net/forum?id=TrjbxzRcnf- | https://openreview.net/forum?id=TrjbxzRcnf- | Yuhuai Wu,Markus Norman Rabe,DeLesley Hutchins,Christian Szegedy | ICLR 2022,Spotlight | Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights.
We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend language models with the ability to memorize the internal representations of past inputs. We demonstrate that an approximate $k$NN lookup into a non-differentiable memory of recent (key, value) pairs improves language modeling across various benchmarks and tasks, including generic webtext (C4), math papers (arXiv), books (PG-19), code (Github), as well as formal theorems (Isabelle). We show that the performance steadily improves when we increase the size of memory up to 262K tokens.
On benchmarks including code and mathematics, we find that the model is capable of making use of newly defined functions and theorems during test time. | https://openreview.net/pdf/33d84d1024126d6a7d4098f2f3beffdbe7057caa.pdf |
Learning-Augmented $k$-means Clustering | https://openreview.net/forum?id=X8cLTHexYyY | https://openreview.net/forum?id=X8cLTHexYyY | Jon C. Ergun,Zhili Feng,Sandeep Silwal,David Woodruff,Samson Zhou | ICLR 2022,Spotlight | $k$-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the $k$-means problem on worst-case inputs. To overcome this barrier, we consider a scenario where ``advice'' is provided to help perform clustering. Specifically, we consider the $k$-means problem augmented with a predictor that, given any point, returns its cluster label in an approximately optimal clustering up to some, possibly adversarial, error. We present an algorithm whose performance improves along with the accuracy of the predictor, even though na\"{i}vely following the accurate predictor can still lead to a high clustering cost. Thus if the predictor is sufficiently accurate, we can retrieve a close to optimal clustering with nearly optimal runtime, breaking known computational barriers for algorithms that do not have access to such advice. We evaluate our algorithms on real datasets and show significant improvements in the quality of clustering. | https://openreview.net/pdf/aec5563bc92ef6f5f5b441eec312315315b468c9.pdf |
On the Uncomputability of Partition Functions in Energy-Based Sequence Models | https://openreview.net/forum?id=SsPCtEY6yCl | https://openreview.net/forum?id=SsPCtEY6yCl | Chu-Cheng Lin,Arya D. McCarthy | ICLR 2022,Spotlight | In this paper, we argue that energy-based sequence models backed by expressive parametric families can result in uncomputable and inapproximable partition functions. Among other things, this makes model selection--and therefore learning model parameters--not only difficult, but generally _undecidable_. The reason is that there are no good deterministic or randomized estimates of partition functions. Specifically, we exhibit a pathological example where under common assumptions, _no_ useful importance sampling estimates of the partition function can guarantee to have variance bounded below a rational number. As alternatives, we consider sequence model families whose partition functions are computable (if they exist), but at the cost of reduced expressiveness. Our theoretical results suggest that statistical procedures with asymptotic guarantees and sheer (but finite) amounts of compute are not the only things that make sequence modeling work; computability concerns must not be neglected as we consider more expressive model parametrizations. | https://openreview.net/pdf/886d78756e355f194640b5e7bf0bfbd8482e5623.pdf |
Perceiver IO: A General Architecture for Structured Inputs & Outputs | https://openreview.net/forum?id=fILj7WpI-g | https://openreview.net/forum?id=fILj7WpI-g | Andrew Jaegle,Sebastian Borgeaud,Jean-Baptiste Alayrac,Carl Doersch,Catalin Ionescu,David Ding,Skanda Koppula,Daniel Zoran,Andrew Brock,Evan Shelhamer,Olivier J Henaff,Matthew Botvinick,Andrew Zisserman,Oriol Vinyals,Joao Carreira | ICLR 2022,Spotlight | A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain & task assumptions or scale poorly to large inputs or outputs. In this work, we propose Perceiver IO, a general-purpose architecture that handles data from arbitrary settings while scaling linearly with the size of inputs and outputs. Our model augments the Perceiver with a flexible querying mechanism that enables outputs of various sizes and semantics, doing away with the need for task-specific architecture engineering. The same architecture achieves strong results on tasks spanning natural language and visual understanding, multi-task and multi-modal reasoning, and StarCraft II. As highlights, Perceiver IO outperforms a Transformer-based BERT baseline on the GLUE language benchmark despite removing input tokenization and achieves state-of-the-art performance on Sintel optical flow estimation with no explicit mechanisms for multiscale correspondence. | https://openreview.net/pdf/be7bf6b12e6abb37fb7853467cc6ef71ea5a1659.pdf |
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization | https://openreview.net/forum?id=POvMvLi91f | https://openreview.net/forum?id=POvMvLi91f | Aviral Kumar,Rishabh Agarwal,Tengyu Ma,Aaron Courville,George Tucker,Sergey Levine | ICLR 2022,Spotlight | Despite overparameterization, deep networks trained via supervised learning are surprisingly easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious solutions that generalize well on test inputs. It is reasonable to surmise that deep reinforcement learning (RL) methods could also benefit from this effect. In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations. Our theoretical analysis shows that when existing models of implicit regularization are applied to temporal difference learning, the resulting derived regularizer favors degenerate solutions with excessive aliasing, in stark contrast to the supervised learning case. We back up these findings empirically, showing that feature representations learned by a deep network value function trained via bootstrapping can indeed become degenerate, aliasing the representations for state-action pairs that appear on either side of the Bellman backup. To address this issue, we derive the form of this implicit regularizer and, inspired by this derivation, propose a simple and effective explicit regularizer, called DR3, that counteracts the undesirable effects of this implicit regularizer. When combined with existing offline RL methods, DR3 substantially improves performance and stability, alleviating unlearning in Atari 2600 games, D4RL domains and robotic manipulation from images. | https://openreview.net/pdf/4682f104a198c9218cf0cdcdbdea5d55d4cf56d8.pdf |
MT3: Multi-Task Multitrack Music Transcription | https://openreview.net/forum?id=iMSjopcOn0p | https://openreview.net/forum?id=iMSjopcOn0p | Joshua P Gardner,Ian Simon,Ethan Manilow,Curtis Hawthorne,Jesse Engel | ICLR 2022,Spotlight | Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requires transcribing multiple instruments simultaneously, all while preserving fine-scale pitch and timing information. Further, many AMT datasets are ``low-resource'', as even expert musicians find music transcription difficult and time-consuming. Thus, prior work has focused on task-specific architectures, tailored to the individual instruments of each task. In this work, motivated by the promising results of sequence-to-sequence transfer learning for low-resource Natural Language Processing (NLP), we demonstrate that a general-purpose Transformer model can perform multi-task AMT, jointly transcribing arbitrary combinations of musical instruments across several transcription datasets. We show this unified training framework achieves high-quality transcription results across a range of datasets, dramatically improving performance for low-resource instruments (such as guitar), while preserving strong performance for abundant instruments (such as piano). Finally, by expanding the scope of AMT, we expose the need for more consistent evaluation metrics and better dataset alignment, and provide a strong baseline for this new direction of multi-task AMT. | https://openreview.net/pdf/d2fbcd8e79c33510066015e1639aa7edbd4a0dac.pdf |
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features | https://openreview.net/forum?id=nHpzE7DqAnG | https://openreview.net/forum?id=nHpzE7DqAnG | Jiuhai Chen,Jonas Mueller,Vassilis N. Ioannidis,Soji Adeshina,Yangkun Wang,Tom Goldstein,David Wipf | ICLR 2022,Spotlight | Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e.g., text-based, categorical, continuous, etc.). In this setting boosted decision trees and related ensembling techniques generally dominate real-world applications involving iid training/test sets. However, when there are relations between samples and the iid assumption is no longer reasonable, it remains unclear how to incorporate these dependencies within existing boosting pipelines. To this end, we propose a generalized framework for combining boosted trees and more general model ensembling techniques, with graph propagation layers that share node/sample information across edges connecting related samples. And unlike previous efforts to integrate graph-based models with boosting, our approach is anchored to a principled meta loss function such that provable convergence can be guaranteed under relatively mild assumptions. Across a variety of benchmarks involving non-iid graph data with tabular node features, our framework achieves comparable or superior performance. | https://openreview.net/pdf/1c7555405291429ac38f31042735a6536dd9bfb5.pdf |
Geometric and Physical Quantities improve E(3) Equivariant Message Passing | https://openreview.net/forum?id=_xwr8gOBeV1 | https://openreview.net/forum?id=_xwr8gOBeV1 | Johannes Brandstetter,Rob Hesselink,Elise van der Pol,Erik J Bekkers,Max Welling | ICLR 2022,Spotlight | Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E($3$) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invariant scalars, but can contain covariant information, such as vectors or tensors. Our model, composed of steerable MLPs, is able to incorporate geometric and physical information in both the message and update functions.
Through the definition of steerable node attributes, the MLPs provide a new class of activation functions for general use with steerable feature fields. We discuss ours and related work through the lens of equivariant non-linear convolutions, which further allows us to pin-point the successful components of SEGNNs: non-linear message aggregation improves upon classic linear (steerable) point convolutions; steerable messages improve upon recent equivariant graph networks that send invariant messages. We demonstrate the effectiveness of our method on several tasks in computational physics and chemistry and provide extensive ablation studies. | https://openreview.net/pdf/65770d511d6363ed18177d5f6ad2c21985a7884b.pdf |
SphereFace2: Binary Classification is All You Need for Deep Face Recognition | https://openreview.net/forum?id=l3SDgUh7qZO | https://openreview.net/forum?id=l3SDgUh7qZO | Yandong Wen,Weiyang Liu,Adrian Weller,Bhiksha Raj,Rita Singh | ICLR 2022,Spotlight | State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-class classification framework. Despite being popular and effective, these methods still have a few shortcomings that limit empirical performance. In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization. Motivated by these limitations, we propose a novel binary classification training framework, termed SphereFace2. In contrast to existing methods, SphereFace2 circumvents the softmax normalization, as well as the corresponding closed-set assumption. This effectively bridges the gap between training and evaluation, enabling the representations to be improved individually by each binary classification task. Besides designing a specific well-performing loss function, we summarize a few general principles for this "one-vs-all" binary classification framework so that it can outperform current competitive methods. Our experiments on popular benchmarks demonstrate that SphereFace2 can consistently outperform state-of-the-art deep face recognition methods. | https://openreview.net/pdf/32df823cca80df310924963131f7d3535aa80a57.pdf |
Boosting Randomized Smoothing with Variance Reduced Classifiers | https://openreview.net/forum?id=mHu2vIds_-b | https://openreview.net/forum?id=mHu2vIds_-b | Miklós Z. Horváth,Mark Niklas Mueller,Marc Fischer,Martin Vechev | ICLR 2022,Spotlight | Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise. In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state-of-the-art results in multiple settings. The key insight of our work is that the reduced variance of ensembles over the perturbations introduced in RS leads to significantly more consistent classifications for a given input. This, in turn, leads to substantially increased certifiable radii for samples close to the decision boundary. Additionally, we introduce key optimizations which enable an up to 55-fold decrease in sample complexity of RS for predetermined radii, thus drastically reducing its computational overhead. Experimentally, we show that ensembles of only 3 to 10 classifiers consistently improve on their strongest constituting model with respect to their average certified radius (ACR) by 5% to 21% on both CIFAR10 and ImageNet, achieving a new state-of-the-art ACR of 0.86 and 1.11, respectively. We release all code and models required to reproduce our results at https://github.com/eth-sri/smoothing-ensembles. | https://openreview.net/pdf/23e2b6f2b0f6bf1e6d39492a2557b0b0357d6fdf.pdf |
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning | https://openreview.net/forum?id=t5EmXZ3ZLR | https://openreview.net/forum?id=t5EmXZ3ZLR | Manuel Nonnenmacher,Thomas Pfeil,Ingo Steinwart,David Reeb | ICLR 2022,Spotlight | Pruning neural networks reduces inference time and memory costs. On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps, are pruned. We devise two novel saliency-based methods for second-order structured pruning (SOSP) which include correlations among all structures and layers. Our main method SOSP-H employs an innovative second-order approximation, which enables saliency evaluations by fast Hessian-vector products. SOSP-H thereby scales like a first-order method despite taking into account the full Hessian. We validate SOSP-H by comparing it to our second method SOSP-I that uses a well-established Hessian approximation, and to numerous state-of-the-art methods. While SOSP-H performs on par or better in terms of accuracy, it has clear advantages in terms of scalability and efficiency. This allowed us to scale SOSP-H to large-scale vision tasks, even though it captures correlations across all layers of the network. To underscore the global nature of our pruning methods, we evaluate their performance not only by removing structures from a pretrained network, but also by detecting architectural bottlenecks. We show that our algorithms allow to systematically reveal architectural bottlenecks, which we then remove to further increase the accuracy of the networks. | https://openreview.net/pdf/27b7b15fa7028267ed543816aadafc963d85b09a.pdf |
Relational Multi-Task Learning: Modeling Relations between Data and Tasks | https://openreview.net/forum?id=8Py-W8lSUgy | https://openreview.net/forum?id=8Py-W8lSUgy | Kaidi Cao,Jiaxuan You,Jure Leskovec | ICLR 2022,Spotlight | A key assumption in multi-task learning is that at the inference time the multi-task model only has access to a given data point but not to the data point’s labels from other tasks. This presents an opportunity to extend multi-task learning to utilize data point’s labels from other auxiliary tasks, and this way improves performance on the new task. Here we introduce a novel relational multi-task learning setting where we leverage data point labels from auxiliary tasks to make more accurate predictions on the new task. We develop MetaLink, where our key innovation is to build a knowledge graph that connects data points and tasks and thus allows us to leverage labels from auxiliary tasks. The knowledge graph consists of two types of nodes: (1) data nodes, where node features are data embeddings computed by the neural network, and (2) task nodes, with the last layer’s weights for each task as node features. The edges in this knowledge graph capture data-task relationships, and the edge label captures the label of a data point on a particular task. Under MetaLink, we reformulate the new task as a link label prediction problem between a data node and a task node. The MetaLink framework provides flexibility to model knowledge transfer from auxiliary task labels to the task of interest. We evaluate MetaLink on 6 benchmark datasets in both biochemical and vision domains. Experiments demonstrate that MetaLink can successfully utilize the relations among different tasks, outperforming the state-of-the-art methods under the proposed relational multi-task learning setting, with up to 27% improvement in ROC AUC. | https://openreview.net/pdf/2fc25baa1e108a6b1e91d4388bfae9d417400e3f.pdf |
CoBERL: Contrastive BERT for Reinforcement Learning | https://openreview.net/forum?id=sRZ3GhmegS | https://openreview.net/forum?id=sRZ3GhmegS | Andrea Banino,Adria Puigdomenech Badia,Jacob C Walker,Tim Scholtes,Jovana Mitrovic,Charles Blundell | ICLR 2022,Spotlight | Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (COBERL), an agent that combines a new contrastive loss and a hybrid LSTM-transformer architecture to tackle the challenge of improving data efficiency. COBERL enables efficient and robust learning from pixels across a wide variety of domains. We use bidirectional masked prediction in combination with a generalization of a recent contrastive method to learn better representations for RL, without the need of hand engineered data augmentations. We find that COBERL consistently improves data efficiency across the full Atari suite, a set of control tasks and a challenging 3D environment, and often it also increases final score performance. | https://openreview.net/pdf/c833364a7435330b3ee8e71a2020d1172e9d3380.pdf |
Optimal Transport for Causal Discovery | https://openreview.net/forum?id=qwBK94cP1y | https://openreview.net/forum?id=qwBK94cP1y | Ruibo Tu,Kun Zhang,Hedvig Kjellstrom,Cheng Zhang | ICLR 2022,Spotlight | To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical process of the static cause-effect pair data. It provides a new dimension for describing static causal discovery tasks while enjoying more freedom for modeling the quantitative causal influences. In particular, we show that Additive Noise Models (ANMs) correspond to volume-preserving pressureless flows. Consequently, based on their velocity field divergence, we introduce a criterion for determining causal direction. With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models. Our method demonstrated state-of-the-art results on both synthetic and causal discovery benchmark datasets. | https://openreview.net/pdf/beb079a1e442ec0e13e5da89161a1f30ba96279f.pdf |
On Bridging Generic and Personalized Federated Learning for Image Classification | https://openreview.net/forum?id=I1hQbx10Kxn | https://openreview.net/forum?id=I1hQbx10Kxn | Hong-You Chen,Wei-Lun Chao | ICLR 2022,Spotlight | Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributions diverge from each other. This divergence further leads to a dilemma: "Should we prioritize the learned model's generic performance (for future use at the server) or its personalized performance (for each client)?" These two, seemingly competing goals have divided the community to focus on one or the other, yet in this paper we show that it is possible to approach both at the same time. Concretely, we propose a novel federated learning framework that explicitly decouples a model's dual duties with two prediction tasks. On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them. On the other hand, we formulate the personalized predictor as a lightweight adaptive module that is learned to minimize each client's empirical risk on top of the generic predictor. With this two-loss, two-predictor framework which we name Federated Robust Decoupling (Fed-RoD), the learned model can simultaneously achieve state-of-the-art generic and personalized performance, essentially bridging the two tasks. | https://openreview.net/pdf/770f89d1e8fedb42b47cd1ac9c8df3df92f3a178.pdf |
Value Gradient weighted Model-Based Reinforcement Learning | https://openreview.net/forum?id=4-D6CZkRXxI | https://openreview.net/forum?id=4-D6CZkRXxI | Claas A Voelcker,Victor Liao,Animesh Garg,Amir-massoud Farahmand | ICLR 2022,Spotlight | Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead performance deterioration. The model in MBRL is often solely fitted to reconstruct dynamics, state observations in particular, while the impact of model error on the policy is not captured by the training objective. This leads to a mismatch between the intended goal of MBRL, enabling good policy and value learning, and the target of the loss function employed in practice, future state prediction. Naive intuition would suggest that value-aware model learning would fix this problem and, indeed, several solutions to this objective mismatch problem have been proposed based on theoretical analysis. However, they tend to be inferior in practice to commonly used maximum likelihood (MLE) based approaches. In this paper we propose the Value-gradient weighted Model Learning (VaGraM), a novel method for value-aware model learning which improves the performance of MBRL in challenging settings, such as small model capacity and the presence of distracting state dimensions. We analyze both MLE and value-aware approaches and demonstrate how they fail to account for exploration and the behavior of function approximation when learning value-aware models and highlight the additional goals that must be met to stabilize optimization in the deep learning setting. We verify our analysis by showing that our loss function is able to achieve high returns on the Mujoco benchmark suite while being more robust than maximum likelihood based approaches.
| https://openreview.net/pdf/d924f4fd00b558974bf7f10d5b94c179c583225b.pdf |
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling | https://openreview.net/forum?id=-llS6TiOew | https://openreview.net/forum?id=-llS6TiOew | Ada Wan | ICLR 2022,Spotlight | We perform systematically and fairly controlled experiments with the 6-layer Transformer to investigate the hardness in conditional-language-modeling languages which have been traditionally considered morphologically rich (AR and RU) and poor (ZH). We evaluate through statistical comparisons across 30 possible language directions from the 6 languages of the United Nations Parallel Corpus across 5 data sizes on 3 representation levels --- character, byte, and word. Results show that performance is relative to the representation granularity of each of the languages, not to the language as a whole. On the character and byte levels, we are able to eliminate statistically significant performance disparity, hence demonstrating that a language cannot be intrinsically hard. The disparity that mirrors the morphological complexity hierarchy is shown to be a byproduct of word segmentation. Evidence from data statistics, along with the fact that word segmentation is qualitatively indeterminate, renders a decades-long debate on morphological complexity (unless it is being intentionally modeled in a word-based, meaning-driven context) irrelevant in the context of computing. The intent of our work is to help effect more objectivity and adequacy in evaluation as well as fairness and inclusivity in experimental setup in the area of language and computing so to uphold diversity in Machine Learning and Artificial Intelligence research. Multilinguality is real and relevant in computing not due to canonical, structural linguistic concepts such as morphology or "words" in our minds, but rather standards related to internationalization and localization, such as character encoding --- something which has thus far been sorely overlooked in our discourse and curricula. | https://openreview.net/pdf/1bc81aec7b25823dcaab95c24c1c5c0779bd3c7c.pdf |
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration | https://openreview.net/forum?id=YJ1WzgMVsMt | https://openreview.net/forum?id=YJ1WzgMVsMt | Desik Rengarajan,Gargi Vaidya,Akshay Sarvesh,Dileep Kalathil,Srinivas Shakkottai | ICLR 2022,Spotlight | A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indicates whether the task is completed partially or fully. However, the lack of carefully designed, fine grain feedback implies that most existing RL algorithms fail to learn an acceptable policy in a reasonable time frame. This is because of the large number of exploration actions that the policy has to perform before it gets any useful feedback that it can learn from. In this work, we address this challenging problem by developing an algorithm that exploits the offline demonstration data generated by {a sub-optimal behavior policy} for faster and efficient online RL in such sparse reward settings. The proposed algorithm, which we call the Learning Online with Guidance Offline (LOGO) algorithm, merges a policy improvement step with an additional policy guidance step by using the offline demonstration data. The key idea is that by obtaining guidance from - not imitating - the offline {data}, LOGO orients its policy in the manner of the sub-optimal {policy}, while yet being able to learn beyond and approach optimality. We provide a theoretical analysis of our algorithm, and provide a lower bound on the performance improvement in each learning episode. We also extend our algorithm to the even more challenging incomplete observation setting, where the demonstration data contains only a censored version of the true state observation. We demonstrate the superior performance of our algorithm over state-of-the-art approaches on a number of benchmark environments with sparse rewards {and censored state}. Further, we demonstrate the value of our approach via implementing LOGO on a mobile robot for trajectory tracking and obstacle avoidance, where it shows excellent performance. | https://openreview.net/pdf/3b163b1af845a3cb05504bfe3d2f3a4a205fe856.pdf |
Linking Emergent and Natural Languages via Corpus Transfer | https://openreview.net/forum?id=49A1Y6tRhaq | https://openreview.net/forum?id=49A1Y6tRhaq | Shunyu Yao,Mo Yu,Yang Zhang,Karthik R Narasimhan,Joshua B. Tenenbaum,Chuang Gan | ICLR 2022,Spotlight | The study of language emergence aims to understand how human languages are shaped by perceptual grounding and communicative intent. Computational approaches to emergent communication (EC) predominantly consider referential games in limited domains and analyze the learned protocol within the game framework. As a result, it remains unclear how the emergent languages from these settings connect to natural languages or provide benefits in real-world language processing tasks, where statistical models trained on large text corpora dominate. In this work, we propose a novel way to establish such a link by corpus transfer, i.e. pretraining on a corpus of emergent language for downstream natural language tasks, which is in contrast to prior work that directly transfers speaker and listener parameters. Our approach showcases non-trivial transfer benefits for two different tasks – language modeling and image captioning. For example, in a low-resource setup (modeling 2 million natural language tokens), pre-training on an emergent language corpus with just 2 million tokens reduces model perplexity by 24.6% on average across ten natural languages. We also introduce a novel metric to predict the transferability of an emergent language by translating emergent messages to natural language captions grounded on the same images. We find that our translation-based metric highly correlates with the downstream performance on modeling natural languages (for instance $\rho = 0.83$ on Hebrew), while topographic similarity, a popular metric in previous works, shows surprisingly low correlation ($\rho = 0.003$), hinting that simple properties like attribute disentanglement from synthetic domains might not capture the full complexities of natural language. Our findings also indicate potential benefits of moving language emergence forward with natural language resources and models. | https://openreview.net/pdf/1a6c8cd5798d4d7939b303c278024594ccda6968.pdf |
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting | https://openreview.net/forum?id=wv6g8fWLX2q | https://openreview.net/forum?id=wv6g8fWLX2q | Yuzhou Chen,Ignacio Segovia-Dominguez,Baris Coskunuzer,Yulia Gel | ICLR 2022,Spotlight | Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex dependencies in multivariate spatio-temporal processes. However, most existing GNNs have inherently static architectures, and as a result, do not explicitly account for time dependencies of the encoded knowledge and are limited in their ability to simultaneously infer latent time-conditioned relations among entities. We postulate that such hidden time-conditioned properties may be captured by the tools of multipersistence, i.e, a emerging machinery in topological data analysis which allows us to quantify dynamics of the data shape along multiple geometric dimensions.
We make the first step toward integrating the two rising research directions, that is, time-aware deep learning and multipersistence, and propose a new model, Time-Aware Multipersistence Spatio-Supra Graph Convolutional Network (TAMP-S2GCNets). We summarize inherent time-conditioned topological properties of the data as time-aware multipersistence Euler-Poincar\'e surface and prove its stability. We then construct a supragraph convolution module which simultaneously accounts for the extracted intra- and inter- spatio-temporal dependencies in the data. Our extensive experiments on highway traffic flow, Ethereum token prices, and COVID-19 hospitalizations demonstrate that TAMP-S2GCNets outperforms the state-of-the-art tools in multivariate time series forecasting tasks. | https://openreview.net/pdf/7e6227c594fe1f760bcc5ecab60120d7439ba995.pdf |
The MultiBERTs: BERT Reproductions for Robustness Analysis | https://openreview.net/forum?id=K0E_F0gFDgA | https://openreview.net/forum?id=K0E_F0gFDgA | Thibault Sellam,Steve Yadlowsky,Ian Tenney,Jason Wei,Naomi Saphra,Alexander D'Amour,Tal Linzen,Jasmijn Bastings,Iulia Raluca Turc,Jacob Eisenstein,Dipanjan Das,Ellie Pavlick | ICLR 2022,Spotlight | Experiments with pre-trained models such as BERT are often based on a single checkpoint. While the conclusions drawn apply to the artifact tested in the experiment (i.e., the particular instance of the model), it is not always clear whether they hold for the more general procedure which includes the architecture, training data, initialization scheme, and loss function. Recent work has shown that repeating the pre-training process can lead to substantially different performance, suggesting that an alternative strategy is needed to make principled statements about procedures. To enable researchers to draw more robust conclusions, we introduce MultiBERTs, a set of 25 BERT-Base checkpoints, trained with similar hyper-parameters as the original BERT model but differing in random weight initialization and shuffling of training data. We also define the Multi-Bootstrap, a non-parametric bootstrap method for statistical inference designed for settings where there are multiple pre-trained models and limited test data. To illustrate our approach, we present a case study of gender bias in coreference resolution, in which the Multi-Bootstrap lets us measure effects that may not be detected with a single checkpoint. The models and statistical library are available online, along with an additional set of 140 intermediate checkpoints captured during pre-training to facilitate research on learning dynamics. | https://openreview.net/pdf/2e66adcaa5191446f5006eb5db26da387f29ec18.pdf |
Message Passing Neural PDE Solvers | https://openreview.net/forum?id=vSix3HPYKSU | https://openreview.net/forum?id=vSix3HPYKSU | Johannes Brandstetter,Daniel E. Worrall,Max Welling | ICLR 2022,Spotlight | The numerical solution of partial differential equations (PDEs) is difficult, having led to a century of research so far. Recently, there have been pushes to build neural--numerical hybrid solvers, which piggy-backs the modern trend towards fully end-to-end learned systems. Most works so far can only generalize over a subset of properties to which a generic solver would be faced, including: resolution, topology, geometry, boundary conditions, domain discretization regularity, dimensionality, etc. In this work, we build a solver, satisfying these properties, where all the components are based on neural message passing, replacing all heuristically designed components in the computation graph with backprop-optimized neural function approximators. We show that neural message passing solvers representationally contain some classical methods, such as finite differences, finite volumes, and WENO schemes. In order to encourage stability in training autoregressive models, we put forward a method that is based on the principle of zero-stability, posing stability as a domain adaptation problem. We validate our method on various fluid-like flow problems, demonstrating fast, stable, and accurate performance across different domain topologies, discretization, etc. in 1D and 2D. Our model outperforms state-of-the-art numerical solvers in the low resolution regime in terms of speed, and accuracy. | https://openreview.net/pdf/dfb3e9b359e53414eb2852a5ed8ed48038f889c0.pdf |
Multi-Stage Episodic Control for Strategic Exploration in Text Games | https://openreview.net/forum?id=Ek7PSN7Y77z | https://openreview.net/forum?id=Ek7PSN7Y77z | Jens Tuyls,Shunyu Yao,Sham M. Kakade,Karthik R Narasimhan | ICLR 2022,Spotlight | Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. This work proposes to tackle the explore-vs-exploit dilemma using a multi-stage approach that explicitly disentangles these two strategies within each episode. Our algorithm, called eXploit-Then-eXplore (XTX), begins each episode using an exploitation policy that imitates a set of promising trajectories from the past, and then switches over to an exploration policy aimed at discovering novel actions that lead to unseen state spaces. This policy decomposition allows us to combine global decisions about which parts of the game space to return to with curiosity-based local exploration in that space, motivated by how a human may approach these games. Our method significantly outperforms prior approaches by 27% and 11% average normalized score over 12 games from the Jericho benchmark (Hausknecht et al., 2020) in both deterministic and stochastic settings, respectively. On the game of Zork1, in particular, XTX obtains a score of 103, more than a 2x improvement over prior methods, and pushes past several known bottlenecks in the game that have plagued previous state-of-the-art methods. | https://openreview.net/pdf/b67d131e9b9afd599358ce78865538bd83521d24.pdf |
Exploring the Limits of Large Scale Pre-training | https://openreview.net/forum?id=V3C8p78sDa | https://openreview.net/forum?id=V3C8p78sDa | Samira Abnar,Mostafa Dehghani,Behnam Neyshabur,Hanie Sedghi | ICLR 2022,Spotlight | Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training would transfer favorably to most downstream tasks. In this work we systematically study this phenomena and establish that, as we increase the upstream accuracy, performance of downstream tasks \emph{saturates}. In particular, we investigate more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with number of parameters ranging from ten million to ten billion, trained on the largest scale of available image data (JFT, ImageNet21K) and evaluated on more than 20 downstream image recognition tasks. We propose a model for downstream performance that reflects the saturation phenomena and captures the nonlinear relationship in performance of upstream and downstream tasks. Delving deeper to understand the reasons that give rise to these phenomena, we show that the observed saturation behavior is closely related to the way that representations evolve through the layers of the models. We showcase an even more extreme scenario where performance on upstream and downstream are at odds with each other. That is, in order to have a better downstream performance, we need to hurt upstream accuracy. | https://openreview.net/pdf/a96ab51b85d9ac8937fe9688a023e72c05b91822.pdf |
Universal Approximation Under Constraints is Possible with Transformers | https://openreview.net/forum?id=JGO8CvG5S9 | https://openreview.net/forum?id=JGO8CvG5S9 | Anastasis Kratsios,Behnoosh Zamanlooy,Tianlin Liu,Ivan Dokmanić | ICLR 2022,Spotlight | Many practical problems need the output of a machine learning model to satisfy a set of constraints, $K$. Nevertheless, there is no known guarantee that classical neural network architectures can exactly encode constraints while simultaneously achieving universality. We provide a quantitative constrained universal approximation theorem which guarantees that for any non-convex compact set $K$ and any continuous function $f:\mathbb{R}^n\rightarrow K$, there is a probabilistic transformer $\hat{F}$ whose randomized outputs all lie in $K$ and whose expected output uniformly approximates $f$. Our second main result is a ``deep neural version'' of Berge's Maximum Theorem (1963). The result guarantees that given an objective function $L$, a constraint set $K$, and a family of soft constraint sets, there is a probabilistic transformer $\hat{F}$ that approximately minimizes $L$ and whose outputs belong to $K$; moreover, $\hat{F}$ approximately satisfies the soft constraints. Our results imply the first universal approximation theorem for classical transformers with exact convex constraint satisfaction. They also yield that a chart-free universal approximation theorem for Riemannian manifold-valued functions subject to suitable geodesically convex constraints. | https://openreview.net/pdf/5b02f8b0bb76868ca513d915646aba6e37d7727e.pdf |
Scaling Laws for Neural Machine Translation | https://openreview.net/forum?id=hR_SMu8cxCV | https://openreview.net/forum?id=hR_SMu8cxCV | Behrooz Ghorbani,Orhan Firat,Markus Freitag,Ankur Bapna,Maxim Krikun,Xavier Garcia,Ciprian Chelba,Colin Cherry | ICLR 2022,Spotlight | We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law. Specifically (i) We propose a formula which describes the scaling behavior of cross-entropy loss as a bivariate function of encoder and decoder size, and show that it gives accurate predictions under a variety of scaling approaches and languages; we show that the total number of parameters alone is not sufficient for such purposes. (ii) We observe different power law exponents when scaling the decoder vs scaling the encoder, and provide recommendations for optimal allocation of encoder/decoder capacity based on this observation. (iii) We also report that the scaling behavior of the model is acutely influenced by composition bias of the train/test sets, which we define as any deviation from naturally generated text (either via machine generated or human translated text). We observe that natural text on the target side enjoys scaling, which manifests as successful reduction of the cross-entropy loss. (iv) Finally, we investigate the relationship between the cross-entropy loss and the quality of the generated translations. We find two different behaviors, depending on the nature of the test data. For test sets which were originally translated from target language to source language, both loss and BLEU score improve as model size increases. In contrast, for test sets originally translated from source language to target language, the loss improves, but the BLEU score stops improving after a certain threshold. We release generated text from all models used in this study. | https://openreview.net/pdf/dec3d7582a0893c49661157564fdbe66ccc0036f.pdf |
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning | https://openreview.net/forum?id=8H5bpVwvt5 | https://openreview.net/forum?id=8H5bpVwvt5 | Biwei Huang,Fan Feng,Chaochao Lu,Sara Magliacane,Kun Zhang | ICLR 2022,Spotlight | One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called AdaRL, that adapts reliably and efficiently to changes across domains with a few samples from the target domain, even in partially observable environments. Specifically, we leverage a parsimonious graphical representation that characterizes structural relationships over variables in the RL system. Such graphical representations provide a compact way to encode what and where the changes across domains are, and furthermore inform us with a minimal set of changes that one has to consider for the purpose of policy adaptation. We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided. We illustrate the efficacy of AdaRL through a series of experiments that vary factors in the observation, transition and reward functions for Cartpole and Atari games. | https://openreview.net/pdf/d3061c36db2595696e3c5444edf46fe3a2f665e9.pdf |
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking | https://openreview.net/forum?id=GQjaI9mLet | https://openreview.net/forum?id=GQjaI9mLet | Octavian-Eugen Ganea,Xinyuan Huang,Charlotte Bunne,Yatao Bian,Regina Barzilay,Tommi S. Jaakkola,Andreas Krause | ICLR 2022,Spotlight | Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no conformational change within the proteins happens during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position relative to the second protein. We mathematically guarantee a basic principle: the predicted complex is always identical regardless of the initial locations and orientations of the two structures. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment, achieved through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements and often outperform existing docking software despite not relying on heavy candidate sampling, structure refinement, or templates. | https://openreview.net/pdf/4b1b65283103b98ee8f765eb00dca5e4786fcf19.pdf |
Towards a Unified View of Parameter-Efficient Transfer Learning | https://openreview.net/forum?id=0RDcd5Axok | https://openreview.net/forum?id=0RDcd5Axok | Junxian He,Chunting Zhou,Xuezhe Ma,Taylor Berg-Kirkpatrick,Graham Neubig | ICLR 2022,Spotlight | Fine-tuning large pretrained language models on downstream tasks has become the de-facto learning paradigm in NLP. However, conventional approaches fine-tune all the parameters of the pretrained model, which becomes prohibitive as the model size and the number of tasks grow. Recent work has proposed a variety of parameter-efficient transfer learning methods that only fine-tune a small number of (extra) parameters to attain strong performance. While effective, the critical ingredients for success and the connections among the various methods are poorly understood. In this paper, we break down the design of state-of-the-art parameter-efficient transfer learning methods and present a unified framework that establishes connections between them. Specifically, we re-frame them as modifications to specific hidden states in pretrained models, and define a set of design dimensions along which different methods vary, such as the function to compute the modification and the position to apply the modification. Through comprehensive empirical studies across machine translation, text summarization, language understanding, and text classification benchmarks, we utilize the unified view to identify important design choices in previous methods. Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks. | https://openreview.net/pdf/859577d9cab3bf7833fe6fd6ea3a66c3b424c6bb.pdf |
GNN-LM: Language Modeling based on Global Contexts via GNN | https://openreview.net/forum?id=BS49l-B5Bql | https://openreview.net/forum?id=BS49l-B5Bql | Yuxian Meng,Shi Zong,Xiaoya Li,Xiaofei Sun,Tianwei Zhang,Fei Wu,Jiwei Li | ICLR 2022,Spotlight | Inspired by the notion that "it to copy is easier than to memorize", in this work, we introduce GNN-LM, which extends vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus. We build a directed heterogeneous graph between an input context and its semantically related neighbors selected from the training corpus, where nodes are tokens in the input context and retrieved neighbor contexts, and edges represent connections between nodes. Graph neural networks (GNNs) are constructed upon the graph to aggregate information from similar contexts to decode the token. This learning paradigm provides direct access to the reference contexts and helps improve a model's generalization ability. We conduct comprehensive experiments to validate the effectiveness of the GNN-LM: GNN-LM achieves a new state-of-the-art perplexity of 14.8 on WikiText-103 (a 3.9 point improvement over its counterpart of the vanilla LM model), and shows substantial improvement on One Billion Word and Enwiki8 datasets against strong baselines. In-depth ablation studies are performed to understand the mechanics of GNN-LM. The code can be found at https://github.com/ShannonAI/GNN-LM. | https://openreview.net/pdf/5a848353a24b880cebcd40d2e65796e794c0ff1d.pdf |
Continual Learning with Filter Atom Swapping | https://openreview.net/forum?id=metRpM4Zrcb | https://openreview.net/forum?id=metRpM4Zrcb | Zichen Miao,Ze Wang,Wei Chen,Qiang Qiu | ICLR 2022,Spotlight | Continual learning has been widely studied in recent years to resolve the catastrophic forgetting of deep neural networks. In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms. Then, we perform continual learning with filter atom swapping. In other words, we learn for each task a new filter subspace for each convolutional layer, i.e., hundreds of parameters as filter atoms, but keep subspace coefficients shared across tasks. By maintaining a small footprint memory of filter atoms, we can easily archive models for past tasks to avoid forgetting. The effectiveness of this simple scheme for continual learning is illustrated both empirically and theoretically. The proposed atom swapping framework further enables flexible and efficient model ensemble with members selected within a task or across tasks to improve the performance in different continual learning settings. Being validated on multiple benchmark datasets with different convolutional network structures, the proposed method outperforms the state-of-the-art methods in both accuracy and scalability. | https://openreview.net/pdf/f453901d83c3373b05abe089d047948fa6e10b9b.pdf |
Continual Learning with Recursive Gradient Optimization | https://openreview.net/forum?id=7YDLgf9_zgm | https://openreview.net/forum?id=7YDLgf9_zgm | Hao Liu,Huaping Liu | ICLR 2022,Spotlight | Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning(CL), remains a long-standing challenge for neural networks. Most existing methods rely on additional network capacity or data replay. In contrast, we introduce a novel approach which we refer to as Recursive Gradient Optimization(RGO). RGO is composed of an iteratively updated optimizer that modifies the gradient to minimize forgetting without data replay and a virtual Feature Encoding Layer(FEL) that represents different long-term structures with only task descriptors. Experiments demonstrate that RGO has significantly better performance on popular continual classification benchmarks when compared to the baselines and achieves new state-of-the-art performance on 20-split-CIFAR100(82.22%) and 20-split-miniImageNet(72.63%). With higher average accuracy than Single-Task Learning(STL), this method is flexible and reliable to provide continual learning capabilities for learning models that rely on gradient descent. | https://openreview.net/pdf/19ab5aa0cb46a13fe9b97dab46913da24e89d2fb.pdf |
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning | https://openreview.net/forum?id=g8NJR6fCCl8 | https://openreview.net/forum?id=g8NJR6fCCl8 | Chun-Hao Chang,Rich Caruana,Anna Goldenberg | ICLR 2022,Spotlight | Deployment of machine learning models in real high-risk settings (e.g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability. Generalized Additive Models (GAMs) are a class of interpretable models with a long history of use in these high-risk domains, but they lack desirable features of deep learning such as differentiability and scalability. In this work, we propose a neural GAM (NODE-GAM) and neural GA$^2$M (NODE-GA$^2$M) that scale well and perform better than other GAMs on large datasets, while remaining interpretable compared to other ensemble and deep learning models. We demonstrate that our models find interesting patterns in the data. Lastly, we show that we are able to improve model accuracy via self-supervised pre-training, an improvement that is not possible for non-differentiable GAMs. | https://openreview.net/pdf/dca44cbebb8f51bf19f423539a640118c674db6f.pdf |
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness | https://openreview.net/forum?id=dgxFTxuJ50e | https://openreview.net/forum?id=dgxFTxuJ50e | Sho Okumoto,Taiji Suzuki | ICLR 2022,Spotlight | Among a wide range of success of deep learning, convolutional neural networks have been extensively utilized in several tasks such as speech recognition, image processing, and natural language processing, which require inputs with large dimensions.
Several studies have investigated function estimation capability of deep learning, but most of them have assumed that the dimensionality of the input is much smaller than the sample size.
However, for typical data in applications such as those handled by the convolutional neural networks described above,
the dimensionality of inputs is relatively high or even infinite.
In this paper, we investigate the approximation and estimation errors of the (dilated) convolutional neural networks when the input is infinite dimensional.
Although the approximation and estimation errors of neural networks are affected by the curse of dimensionality in the existing analyses for typical function spaces such as the \Holder and Besov spaces, we show that, by considering anisotropic smoothness, they can alleviate exponential dependency on the dimensionality but they only depend on the smoothness of the target functions.
Our theoretical analysis supports the great practical success of convolutional networks.
Furthermore, we show that the dilated convolution is advantageous when the smoothness of the target function has a sparse structure. | https://openreview.net/pdf/634d72e29913c5641e4d1a3370a43676421f5b75.pdf |
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100 | https://openreview.net/forum?id=tD7eCtaSkR | https://openreview.net/forum?id=tD7eCtaSkR | Sahil Singla,Surbhi Singla,Soheil Feizi | ICLR 2022,Spotlight | Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the $l_{2}$ norm is useful for provable adversarial robustness, interpretable gradients and stable training. While $1$-Lipschitz CNNs can be designed by enforcing a $1$-Lipschitz constraint on each layer, training such networks requires each layer to have an orthogonal Jacobian matrix (for all inputs) to prevent the gradients from vanishing during backpropagation. A layer with this property is said to be Gradient Norm Preserving (GNP). In this work, we introduce a procedure to certify the robustness of $1$-Lipschitz CNNs by relaxing the orthogonalization of the last linear layer of the network that significantly advances the state of the art for both standard and provable robust accuracies on CIFAR-100 (gains of $4.80\%$ and $4.71\%$, respectively). We further boost their robustness by introducing (i) a novel Gradient Norm preserving activation function called the Householder activation function (that includes every $\mathrm{GroupSort}$ activation) and (ii) a certificate regularization. On CIFAR-10, we achieve significant improvements over prior works in provable robust accuracy ($5.81\%$) with only a minor drop in standard accuracy ($-0.29\%$). Code for reproducing all experiments in the paper is available at \url{https://github.com/singlasahil14/SOC}. | https://openreview.net/pdf/666d6306f372e8bad4486c511aded6081ad8c921.pdf |
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models | https://openreview.net/forum?id=CyKHoKyvgnp | https://openreview.net/forum?id=CyKHoKyvgnp | Chaoyue Liu,Libin Zhu,Misha Belkin | ICLR 2022,Spotlight | Wide neural networks with linear output layer have been shown to be near-linear, and to have near-constant neural tangent kernel (NTK), in a region containing the optimization path of gradient descent. These findings seem counter-intuitive since in general neural networks are highly complex models. Why does a linear structure emerge when the neural networks become wide?
In this work, we provide a new perspective on this "transition to linearity" by considering a neural network as an assembly model recursively built from a set of sub-models corresponding to individual neurons. In this view, we show that the linearity of wide neural networks is, in fact, an emerging property of assembling a large number of diverse ``weak'' sub-models, none of which dominate the assembly. | https://openreview.net/pdf/d4e2a924c1b35ff107a22198b053db66c9f1ae3d.pdf |
Looking Back on Learned Experiences For Class/task Incremental Learning | https://openreview.net/forum?id=RxplU3vmBx | https://openreview.net/forum?id=RxplU3vmBx | Mozhgan PourKeshavarzi,Guoying Zhao,Mohammad Sabokrou | ICLR 2022,Spotlight | Classical deep neural networks are limited in their ability to learn from emerging streams of training data. When trained sequentially on new or evolving tasks, their performance degrades sharply, making them inappropriate in real-world use cases. Existing methods tackle it by either storing old data samples or only updating a parameter set of deep neural networks, which, however, demands a large memory budget or spoils the flexibility of models to learn the incremented task distribution. In this paper, we shed light on an on-call transfer set to provide past experiences whenever a new task arises in the data stream. In particular, we propose a Cost-Free Incremental Learning (CF-IL) not only to replay past experiences the model has learned but also to perform this in a cost free manner. Towards this end, we introduced a memory recovery paradigm in which we query the network to synthesize past exemplars whenever a new task emerges. Thus, our method needs no extra memory for data buffering or network growing, besides calls the proposed memory recovery paradigm to provide past exemplars, named a transfer set in order to mitigate catastrophically forgetting the former tasks in the Incremental Learning (IL) setup. Moreover, in contrast with recently proposed methods, the suggested paradigm does not desire a parallel architecture since it only relies on the learner network. Compared to the state-of-the-art data techniques without buffering past data samples, CF-IL demonstrates significantly better performance on the well-known datasets whether a task oracle is available in test time (Task-IL) or not (Class-IL). | https://openreview.net/pdf/6a7c60c7a81fef15a85782af93ea2e117db6ac6b.pdf |
EntQA: Entity Linking as Question Answering | https://openreview.net/forum?id=US2rTP5nm_ | https://openreview.net/forum?id=US2rTP5nm_ | Wenzheng Zhang,Wenyue Hua,Karl Stratos | ICLR 2022,Spotlight | A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base. A well-known limitation of this approach is that it requires finding mentions without knowing their entities, which is unnatural and difficult. We present a new model that does not suffer from this limitation called $\textbf{EntQA}$, which stands for $\mbox{\textbf{Ent}ity}$ linking as $\mbox{\textbf{Q}uestion}$ $\mbox{\textbf{A}nswering}$. EntQA first proposes candidate entities with a fast retrieval module, and then scrutinizes the document to find mentions of each candidate with a powerful reader module. Our approach combines progress in entity linking with that in open-domain question answering and capitalizes on pretrained models for dense entity retrieval and reading comprehension. Unlike in previous works, we do not rely on a mention-candidates dictionary or large-scale weak supervision. EntQA achieves strong results on the GERBIL benchmarking platform.
| https://openreview.net/pdf/644a6b82d054eb9827a94841c9a51dc4ba75e7b4.pdf |
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems | https://openreview.net/forum?id=2_vhkAMARk | https://openreview.net/forum?id=2_vhkAMARk | Thomas Pethick,Puya Latafat,Panos Patrinos,Olivier Fercoq,Volkan Cevher | ICLR 2022,Spotlight | This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax problems is computationally intractable. This observation has recently motivated the study of structures sufficient for convergence of first order methods in the more general setting of variational inequalities when the so-called weak Minty variational inequality (MVI) holds. This problem class captures non-trivial structures as we demonstrate with examples, for which a large family of existing algorithms provably converge to limit cycles. Our results require a less restrictive parameter range in the weak MVI compared to what is previously known, thus extending the applicability of our scheme. The proposed algorithm is applicable to constrained and regularized problems, and involves an adaptive stepsize allowing for potentially larger stepsizes. Our scheme also converges globally even in settings where the underlying operator exhibits limit cycles. | https://openreview.net/pdf/2b1d67d765bf24cb72760db6f0bcaff4cefc8032.pdf |
Compositional Attention: Disentangling Search and Retrieval | https://openreview.net/forum?id=IwJPj2MBcIa | https://openreview.net/forum?id=IwJPj2MBcIa | Sarthak Mittal,Sharath Chandra Raparthy,Irina Rish,Yoshua Bengio,Guillaume Lajoie | ICLR 2022,Spotlight | Multi-head, key-value attention is the backbone of transformer-like model architectures which have proven to be widely successful in recent years. This attention mechanism uses multiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - selection of a relevant entity from a set via query-key interaction, and (2) retrieval - extraction of relevant features from the selected entity via a value matrix. Standard attention heads learn a rigid mapping between search and retrieval. In this work, we first highlight how this static nature of the pairing can potentially: (a) lead to learning of redundant parameters in certain tasks, and (b) hinder generalization. To alleviate this problem, we propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure. The proposed mechanism disentangles search and retrieval and composes them in a dynamic, flexible and context-dependent manner. Through a series of numerical experiments, we show that it outperforms standard multi-head attention on a variety of tasks, including some out-of-distribution settings. Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed. Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval and is easy to implement in a variety of established network architectures. | https://openreview.net/pdf/0ac34f592d10eb8cc8a3486322f340c5e0a456ba.pdf |
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks | https://openreview.net/forum?id=XWODe7ZLn8f | https://openreview.net/forum?id=XWODe7ZLn8f | Yunji Kim,Jung-Woo Ha | ICLR 2022,Spotlight | Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-GAN, a method that leverages the categorical inference power of InfoGAN with contrastive learning. We aim to learn feature representations that encourage a dataset to form distinct cluster boundaries in the embedding space, while also maximizing the mutual information between the latent code and its image observation. Our approach is to train a discriminator, which is also used for inferring clusters, to optimize the contrastive loss, where image-latent pairs that maximize the mutual information are considered as positive pairs and the rest as negative pairs. Specifically, we map the input of a generator, which was sampled from the categorical distribution, to the embedding space of the discriminator and let them act as a cluster centroid. In this way, C3-GAN succeeded in learning a clustering-friendly embedding space where each cluster is distinctively separable. Experimental results show that C3-GAN achieved the state-of-the-art clustering performance on four fine-grained image datasets, while also alleviating the mode collapse phenomenon. Code is available at https://github.com/naver-ai/c3-gan. | https://openreview.net/pdf/e0f34f45a561d5470192a2a430af4c5ad2ade5d9.pdf |
Learning Multimodal VAEs through Mutual Supervision | https://openreview.net/forum?id=1xXvPrAshao | https://openreview.net/forum?id=1xXvPrAshao | Tom Joy,Yuge Shi,Philip Torr,Tom Rainforth,Sebastian M Schmon,Siddharth N | ICLR 2022,Spotlight | Multimodal VAEs seek to model the joint distribution over heterogeneous data (e.g.\ vision, language), whilst also capturing a shared representation across such modalities. Prior work has typically combined information from the modalities by reconciling idiosyncratic representations directly in the recognition model through explicit products, mixtures, or other such factorisations. Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision. This formulation naturally allows learning from partially-observed data where some modalities can be entirely missing---something that most existing approaches either cannot handle, or do so to a limited extent. We demonstrate that MEME outperforms baselines on standard metrics across both partial and complete observation schemes on the MNIST-SVHN (image--image) and CUB (image--text) datasets. We also contrast the quality of the representations learnt by mutual supervision against standard approaches and observe interesting trends in its ability to capture relatedness between data. | https://openreview.net/pdf/6e3f8005627ed71e89af82b1e6d063771b707c3e.pdf |
When should agents explore? | https://openreview.net/forum?id=dEwfxt14bca | https://openreview.net/forum?id=dEwfxt14bca | Miruna Pislar,David Szepesvari,Georg Ostrovski,Diana L Borsa,Tom Schaul | ICLR 2022,Spotlight | Exploration remains a central challenge for reinforcement learning (RL). Virtually all existing methods share the feature of a *monolithic* behaviour policy that changes only gradually (at best). In contrast, the exploratory behaviours of animals and humans exhibit a rich diversity, namely including forms of *switching* between modes. This paper presents an initial study of mode-switching, non-monolithic exploration for RL. We investigate different modes to switch between, at what timescales it makes sense to switch, and what signals make for good switching triggers. We also propose practical algorithmic components that make the switching mechanism adaptive and robust, which enables flexibility without an accompanying hyper-parameter-tuning burden. Finally, we report a promising initial study on Atari, using two-mode exploration and switching at sub-episodic time-scales. | https://openreview.net/pdf/5628a68890630dcf1b41b8c21140287bb6d2e9eb.pdf |
Revisiting Design Choices in Offline Model Based Reinforcement Learning | https://openreview.net/forum?id=zz9hXVhf40 | https://openreview.net/forum?id=zz9hXVhf40 | Cong Lu,Philip Ball,Jack Parker-Holder,Michael Osborne,Stephen J. Roberts | ICLR 2022,Spotlight | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model. This typically involves constructing a probabilistic model, and using the model uncertainty to penalize rewards where there is insufficient data, solving for a pessimistic MDP that lower bounds the true MDP. Existing methods, however, exhibit a breakdown between theory and practice, whereby pessimistic return ought to be bounded by the total variation distance of the model from the true dynamics, but is instead implemented through a penalty based on estimated model uncertainty. This has spawned a variety of uncertainty heuristics, with little to no comparison between differing approaches. In this paper, we compare these heuristics, and design novel protocols to investigate their interaction with other hyperparameters, such as the number of models, or imaginary rollout horizon. Using these insights, we show that selecting these key hyperparameters using Bayesian Optimization produces superior configurations that are vastly different to those currently used in existing hand-tuned state-of-the-art methods, and result in drastically stronger performance. | https://openreview.net/pdf/1fd710c82e5202735a840dcabdf897afa2030b34.pdf |
NASPY: Automated Extraction of Automated Machine Learning Models | https://openreview.net/forum?id=KhLK0sHMgXK | https://openreview.net/forum?id=KhLK0sHMgXK | Xiaoxuan Lou,Shangwei Guo,Jiwei Li,Yaoxin Wu,Tianwei Zhang | ICLR 2022,Spotlight | We present NASPY, an end-to-end adversarial framework to extract the networkarchitecture of deep learning models from Neural Architecture Search (NAS). Existing works about model extraction attacks mainly focus on conventional DNN models with very simple operations, or require heavy manual analysis with lots of domain knowledge. In contrast, NASPY introduces seq2seq models to automatically identify novel and complicated operations (e.g., separable convolution,dilated convolution) from hardware side-channel sequences. We design two models (RNN-CTC and transformer), which can achieve only 3.2% and 11.3% error rates for operation prediction. We further present methods to recover the model hyper-parameters and topology from the operation sequence . With these techniques, NASPY is able to extract the complete NAS model architecture with high fidelity and automation, which are rarely analyzed before. | https://openreview.net/pdf/86d83b714af5dd7905a8be1ae5d3722feeb303ab.pdf |
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation | https://openreview.net/forum?id=FLA55mBee6Q | https://openreview.net/forum?id=FLA55mBee6Q | Jongmin Lee,Cosmin Paduraru,Daniel J Mankowitz,Nicolas Heess,Doina Precup,Kee-Eung Kim,Arthur Guez | ICLR 2022,Spotlight | We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost constraints, learning only from a pre-collected dataset. This problem setting is appealing in many real-world scenarios, where direct interaction with the environment is costly or risky, and where the resulting policy should comply with safety constraints. However, it is challenging to compute a policy that guarantees satisfying the cost constraints in the offline RL setting, since the off-policy evaluation inherently has an estimation error. In this paper, we present an offline constrained RL algorithm that optimizes the policy in the space of the stationary distribution. Our algorithm, COptiDICE, directly estimates the stationary distribution corrections of the optimal policy with respect to returns, while constraining the cost upper bound, with the goal of yielding a cost-conservative policy for actual constraint satisfaction. Experimental results show that COptiDICE attains better policies in terms of constraint satisfaction and return-maximization, outperforming baseline algorithms. | https://openreview.net/pdf/072227698fafd08a9854a8816e3cc5d5a8eb5754.pdf |
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers | https://openreview.net/forum?id=nhnJ3oo6AB | https://openreview.net/forum?id=nhnJ3oo6AB | Ruihan Yang,Minghao Zhang,Nicklas Hansen,Huazhe Xu,Xiaolong Wang | ICLR 2022,Spotlight | We propose to address quadrupedal locomotion tasks using Reinforcement Learning (RL) with a Transformer-based model that learns to combine proprioceptive information and high-dimensional depth sensor inputs. While learning-based locomotion has made great advances using RL, most methods still rely on domain randomization for training blind agents that generalize to challenging terrains. Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead. In this paper, we introduce LocoTransformer, an end-to-end RL method that leverages both proprioceptive states and visual observations for locomotion control. We evaluate our method in challenging simulated environments with different obstacles and uneven terrain. We transfer our learned policy from simulation to a real robot by running it indoor and in-the-wild with unseen obstacles and terrain. Our method not only significantly improves over baselines, but also achieves far better generalization performance, especially when transferred to the real robot. Our project page with videos is at https://rchalyang.github.io/LocoTransformer/. | https://openreview.net/pdf/44a74037c4089730c34de558a6d679925f866a56.pdf |
ViTGAN: Training GANs with Vision Transformers | https://openreview.net/forum?id=dwg5rXg1WS_ | https://openreview.net/forum?id=dwg5rXg1WS_ | Kwonjoon Lee,Huiwen Chang,Lu Jiang,Han Zhang,Zhuowen Tu,Ce Liu | ICLR 2022,Spotlight | Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases. In this paper, we investigate if such performance can be extended to image generation. To this end, we integrate the ViT architecture into generative adversarial networks (GANs). For ViT discriminators, we observe that existing regularization methods for GANs interact poorly with self-attention, causing serious instability during training. To resolve this issue, we introduce several novel regularization techniques for training GANs with ViTs. For ViT generators, we examine architectural choices for latent and pixel mapping layers to faciliate convergence. Empirically, our approach, named ViTGAN, achieves comparable performance to the leading CNN- based GAN models on three datasets: CIFAR-10, CelebA, and LSUN bedroom. | https://openreview.net/pdf/12b5f82c142cc00c08e950c5a49db1948f14aa54.pdf |
POETREE: Interpretable Policy Learning with Adaptive Decision Trees | https://openreview.net/forum?id=AJsI-ymaKn_ | https://openreview.net/forum?id=AJsI-ymaKn_ | Alizée Pace,Alex Chan,Mihaela van der Schaar | ICLR 2022,Spotlight | Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems. | https://openreview.net/pdf/18b6779789f76ccc8b7ff5e0a77ec0fa2a5d4057.pdf |
TRGP: Trust Region Gradient Projection for Continual Learning | https://openreview.net/forum?id=iEvAf8i6JjO | https://openreview.net/forum?id=iEvAf8i6JjO | Sen Lin,Li Yang,Deliang Fan,Junshan Zhang | ICLR 2022,Spotlight | Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of the new task for minimizing the interference to old tasks. However, this may lead to unsatisfactory performance for the new task, especially when the new task is strongly correlated with old tasks. To tackle this challenge, we propose Trust Region Gradient Projection (TRGP) for continual learning to facilitate the forward knowledge transfer based on an efficient characterization of task correlation. Particularly, we introduce a notion of 'trust region' to select the most related old tasks for the new task in a layer-wise and single-shot manner, using the norm of gradient projection onto the subspace spanned by task inputs. Then, a scaled weight projection is proposed to cleverly reuse the frozen weights of the selected old tasks in the trust region through a layer-wise scaling matrix. By jointly optimizing the scaling matrices and the model, where the model is updated along the directions orthogonal to the subspaces of old tasks, TRGP can effectively prompt knowledge transfer without forgetting. Extensive experiments show that our approach achieves significant improvement over related state-of-the-art methods. | https://openreview.net/pdf/7291715ef56df6f6a92491a5977eb95a2db1ca86.pdf |
Properties from mechanisms: an equivariance perspective on identifiable representation learning | https://openreview.net/forum?id=g5ynW-jMq4M | https://openreview.net/forum?id=g5ynW-jMq4M | Kartik Ahuja,Jason Hartford,Yoshua Bengio | ICLR 2022,Spotlight | A key goal of unsupervised representation learning is ``inverting'' a data generating process to recover its latent properties. Existing work that provably achieves this goal relies on strong assumptions on relationships between the latent variables (e.g., independence conditional on auxiliary information). In this paper, we take a very different perspective on the problem and ask, ``Can we instead identify latent properties by leveraging knowledge of the mechanisms that govern their evolution?'' We provide a complete characterization of the sources of non-identifiability as we vary knowledge about a set of possible mechanisms. In particular, we prove that if we know the exact mechanisms under which the latent properties evolve, then identification can be achieved up to any equivariances that are shared by the underlying mechanisms. We generalize this characterization to settings where we only know some hypothesis class over possible mechanisms, as well as settings where the mechanisms are stochastic. We demonstrate the power of this mechanism-based perspective by showing that we can leverage our results to generalize existing identifiable representation learning results. These results suggest that by exploiting inductive biases on mechanisms, it is possible to design a range of new identifiable representation learning approaches. | https://openreview.net/pdf/2b2b18f71be3fd973de88f72c37e764d69dde18a.pdf |
Revisiting Over-smoothing in BERT from the Perspective of Graph | https://openreview.net/forum?id=dUV91uaXm3 | https://openreview.net/forum?id=dUV91uaXm3 | Han Shi,JIAHUI GAO,Hang Xu,Xiaodan Liang,Zhenguo Li,Lingpeng Kong,Stephen M. S. Lee,James Kwok | ICLR 2022,Spotlight | Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate the main cause of this phenomenon. In this work, we make the attempt to analyze the over-smoothing problem from the perspective of graph, where such problem was first discovered and explored. Intuitively, the self-attention matrix can be seen as a normalized adjacent matrix of a corresponding graph. Based on the above connection, we provide some theoretical analysis and find that layer normalization plays a key role in the over-smoothing issue of Transformer-based models. Specifically, if the standard deviation of layer normalization is sufficiently large, the output of Transformer stacks will converge to a specific low-rank subspace and result in over-smoothing. To alleviate the over-smoothing problem, we consider hierarchical fusion strategies, which combine the representations from different layers adaptively to make the output more diverse. Extensive experiment results on various data sets illustrate the effect of our fusion method. | https://openreview.net/pdf/c6d11173508af7016a361ef28688912c6b0a80a8.pdf |
Training invariances and the low-rank phenomenon: beyond linear networks | https://openreview.net/forum?id=XEW8CQgArno | https://openreview.net/forum?id=XEW8CQgArno | Thien Le,Stefanie Jegelka | ICLR 2022,Spotlight | The implicit bias induced by the training of neural networks has become a topic of rigorous study. In the limit of gradient flow and gradient descent with appropriate step size, it has been shown that when one trains a deep linear network with logistic or exponential loss on linearly separable data, the weights converge to rank-$1$ matrices. In this paper, we extend this theoretical result to the last few linear layers of the much wider class of nonlinear ReLU-activated feedforward networks containing fully-connected layers and skip connections. Similar to the linear case, the proof relies on specific local training invariances, sometimes referred to as alignment, which we show to hold for submatrices where neurons are stably-activated in all training examples, and it reflects empirical results in the literature. We also show this is not true in general for the full matrix of ReLU fully-connected layers. Our proof relies on a specific decomposition of the network into a multilinear function and another ReLU network whose weights are constant under a certain parameter directional convergence. | https://openreview.net/pdf/09cd317c13e66c6829a08a5b598324211771fd4b.pdf |
Learning Long-Term Reward Redistribution via Randomized Return Decomposition | https://openreview.net/forum?id=lpkGn3k2YdD | https://openreview.net/forum?id=lpkGn3k2YdD | Zhizhou Ren,Ruihan Guo,Yuan Zhou,Jian Peng | ICLR 2022,Spotlight | Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms. | https://openreview.net/pdf/9d37ef647d9bba0c4b6fe1976563d07da74d311e.pdf |
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework | https://openreview.net/forum?id=siCt4xZn5Ve | https://openreview.net/forum?id=siCt4xZn5Ve | Zhiyuan Li,Tianhao Wang,Sanjeev Arora | ICLR 2022,Spotlight | Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function $L$ can form a manifold. Intuitively, with a sufficiently small learning rate $\eta$, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, $\text{tr}[\nabla^2 L]$. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold---i.e., the "implicit bias"---using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results: (1) a *global* analysis of the implicit bias valid for $\eta^{-2}$ steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for $\eta^{-1.6}$ steps and (2) allowing *arbitrary* noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires $O(\kappa\ln d)$ samples for learning an $\kappa$-sparse overparametrized linear model in $\mathbb{R}^d$ (Woodworth et al., 2020), while GD initialized in the kernel regime requires $\Omega(d)$ samples. This upper bound is minimax optimal and improves the previous $\widetilde{O}(\kappa^2)$ upper bound (HaoChen et al., 2020). | https://openreview.net/pdf/053e833d0de895f0839c04aabd053b4d678ffb02.pdf |
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series | https://openreview.net/forum?id=45L_dgP48Vd | https://openreview.net/forum?id=45L_dgP48Vd | Enyan Dai,Jie Chen | ICLR 2022,Spotlight | Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift. | https://openreview.net/pdf/b66a4e577ef3e2caf4cb8c17ee02ca6b15eaca26.pdf |
Autoregressive Quantile Flows for Predictive Uncertainty Estimation | https://openreview.net/forum?id=z1-I6rOKv1S | https://openreview.net/forum?id=z1-I6rOKv1S | Phillip Si,Allan Bishop,Volodymyr Kuleshov | ICLR 2022,Spotlight | Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of normalizing flow models trained using a novel objective based on proper scoring rules. Our objective does not require calculating computationally expensive determinants of Jacobians during training and supports new types of neural architectures, such as neural autoregressive flows from which it is easy to sample.
We leverage these models in quantile flow regression, an approach that parameterizes predictive conditional distributions with flows, resulting in improved probabilistic predictions on tasks such as time series forecasting and object detection.
Our novel objective functions and neural flow parameterizations also yield improvements on popular generation and density estimation tasks, and represent a step beyond maximum likelihood learning of flows. | https://openreview.net/pdf/564438790b7385df4f30b72aafb410555a14f948.pdf |
On the Importance of Firth Bias Reduction in Few-Shot Classification | https://openreview.net/forum?id=DNRADop4ksB | https://openreview.net/forum?id=DNRADop4ksB | Saba Ghaffari,Ehsan Saleh,David Forsyth,Yu-Xiong Wang | ICLR 2022,Spotlight | Learning accurate classifiers for novel categories from very few examples, known as few-shot image classification, is a challenging task in statistical machine learning and computer vision. The performance in few-shot classification suffers from the bias in the estimation of classifier parameters; however, an effective underlying bias reduction technique that could alleviate this issue in training few-shot classifiers has been overlooked. In this work, we demonstrate the effectiveness of Firth bias reduction in few-shot classification. Theoretically, Firth bias reduction removes the $O(N^{-1})$ first order term from the small-sample bias of the Maximum Likelihood Estimator. Here we show that the general Firth bias reduction technique simplifies to encouraging uniform class assignment probabilities for multinomial logistic classification, and almost has the same effect in cosine classifiers. We derive an easy-to-implement optimization objective for Firth penalized multinomial logistic and cosine classifiers, which is equivalent to penalizing the cross-entropy loss with a KL-divergence between the predictions and the uniform label distribution. Then, we empirically evaluate that it is consistently effective across the board for few-shot image classification, regardless of (1) the feature representations from different backbones, (2) the number of samples per class, and (3) the number of classes. Furthermore, we demonstrate the effectiveness of Firth bias reduction on cross-domain and imbalanced data settings. Our implementation is available at https://github.com/ehsansaleh/firth_bias_reduction. | https://openreview.net/pdf/fafc00da00fec3e0c8db049a4e2e14a588fa4aa7.pdf |
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward | https://openreview.net/forum?id=04pGUg0-pdZ | https://openreview.net/forum?id=04pGUg0-pdZ | FNU Hairi,Jia Liu,Songtao Lu | ICLR 2022,Spotlight | In this paper, we establish the first finite-time convergence result of the actor-critic algorithm for fully decentralized multi-agent reinforcement learning (MARL) problems with average reward.
In this problem, a set of $N$ agents work cooperatively to maximize the global average reward through interacting with their neighbors over a communication network.
We consider a practical MARL setting, where the rewards and actions of each agent are only known to itself, and the knowledge of joint actions of the agents is not assumed.
Toward this end, we propose a mini-batch Markovian sampled fully decentralized actor-critic algorithm and analyze its finite-time convergence and sample complexity.
We show that the sample complexity of this algorithm is $\mathcal{O}(N^{2}/\epsilon^{2}\log(N/\epsilon))$.
Interestingly, this sample complexity bound matches that of the state-of-the-art single-agent actor-critic algorithms for reinforcement learning. | https://openreview.net/pdf/88771b44b2b7e3d534226c24b2a2e7d9739fc960.pdf |
Towards Understanding the Data Dependency of Mixup-style Training | https://openreview.net/forum?id=ieNJYujcGDO | https://openreview.net/forum?id=ieNJYujcGDO | Muthu Chidambaram,Xiang Wang,Yuzheng Hu,Chenwei Wu,Rong Ge | ICLR 2022,Spotlight | In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training. In this paper, we investigate how these benefits of Mixup training rely on properties of the data in the context of classification. For minimizing the original empirical risk, we compute a closed form for the Mixup-optimal classification, which allows us to construct a simple dataset on which minimizing the Mixup loss leads to learning a classifier that does not minimize the empirical loss on the data. On the other hand, we also give sufficient conditions for Mixup training to also minimize the original empirical risk. For generalization, we characterize the margin of a Mixup classifier, and use this to understand why the decision boundary of a Mixup classifier can adapt better to the full structure of the training data when compared to standard training. In contrast, we also show that, for a large class of linear models and linearly separable datasets, Mixup training leads to learning the same classifier as standard training. | https://openreview.net/pdf/da2d0598034ce31ec61b7322e921e2900518e9f3.pdf |
Self-Supervision Enhanced Feature Selection with Correlated Gates | https://openreview.net/forum?id=oDFvtxzPOx | https://openreview.net/forum?id=oDFvtxzPOx | Changhee Lee,Fergus Imrie,Mihaela van der Schaar | ICLR 2022,Spotlight | Discovering relevant input features for predicting a target variable is a key scientific question. However, in many domains, such as medicine and biology, feature selection is confounded by a scarcity of labeled samples coupled with significant correlations among features. In this paper, we propose a novel deep learning approach to feature selection that addresses both challenges simultaneously. First, we pre-train the network using unlabeled samples within a self-supervised learning framework by solving pretext tasks that require the network to learn informative representations from partial feature sets. Then, we fine-tune the pre-trained network to discover relevant features using labeled samples. During both training phases, we explicitly account for the correlation structure of the input features by generating correlated gate vectors from a multivariate Bernoulli distribution. Experiments on multiple real-world datasets including clinical and omics demonstrate that our model discovers relevant features that provide superior prediction performance compared to the state-of-the-art benchmarks in practical scenarios where there is often limited labeled data and high correlations among features. | https://openreview.net/pdf/eeb0bd632ffa4f23e0076ad9369b3943bbf31efc.pdf |
Score-Based Generative Modeling with Critically-Damped Langevin Diffusion | https://openreview.net/forum?id=CzceR82CYc | https://openreview.net/forum?id=CzceR82CYc | Tim Dockhorn,Arash Vahdat,Karsten Kreis | ICLR 2022,Spotlight | Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise. The complexity of this denoising task is, apart from the data distribution itself, uniquely determined by the diffusion process. We argue that current SGMs employ overly simplistic diffusions, leading to unnecessarily complex denoising processes, which limit generative modeling performance. Based on connections to statistical mechanics, we propose a novel critically-damped Langevin diffusion (CLD) and show that CLD-based SGMs achieve superior performance. CLD can be interpreted as running a joint diffusion in an extended space, where the auxiliary variables can be considered "velocities" that are coupled to the data variables as in Hamiltonian dynamics. We derive a novel score matching objective for CLD and show that the model only needs to learn the score function of the conditional distribution of the velocity given data, an easier task than learning scores of the data directly. We also derive a new sampling scheme for efficient synthesis from CLD-based diffusion models. We find that CLD outperforms previous SGMs in synthesis quality for similar network architectures and sampling compute budgets. We show that our novel sampler for CLD significantly outperforms solvers such as Euler–Maruyama. Our framework provides new insights into score-based denoising diffusion models and can be readily used for high-resolution image synthesis. Project page and code: https://nv-tlabs.github.io/CLD-SGM. | https://openreview.net/pdf/b0abf219a709e60170ee3d79e0068b682e8696dd.pdf |
Controlling Directions Orthogonal to a Classifier | https://openreview.net/forum?id=DIjCrlsu6Z | https://openreview.net/forum?id=DIjCrlsu6Z | Yilun Xu,Hao He,Tianxiao Shen,Tommi S. Jaakkola | ICLR 2022,Spotlight | We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. While orthogonal decomposition is directly identifiable when the given classifier is linear, we formally define a notion of orthogonality in the non-linear case. We also provide a surprisingly simple method for constructing the orthogonal classifier (a classifier utilizing directions other than those of the given classifier). Empirically, we present three use cases where controlling orthogonal variation is important: style transfer, domain adaptation, and fairness. The orthogonal classifier enables desired style transfer when domains vary in multiple aspects, improves domain adaptation with label shifts and mitigates the unfairness as a predictor. The code is available at https://github.com/Newbeeer/orthogonal_classifier | https://openreview.net/pdf/259173e97dc2f0da3f9b4879faaede603f13d98a.pdf |
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