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Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU | https://openreview.net/forum?id=lqU2cs3Zca | https://openreview.net/forum?id=lqU2cs3Zca | Patrick Kidger,Terry Lyons | ICLR 2021,Poster | Signatory is a library for calculating and performing functionality related to the signature and logsignature transforms. The focus is on machine learning, and as such includes features such as CPU parallelism, GPU support, and backpropagation. To our knowledge it is the first GPU-capable library for these operations. Signatory implements new features not available in previous libraries, such as efficient precomputation strategies. Furthermore, several novel algorithmic improvements are introduced, producing substantial real-world speedups even on the CPU without parallelism. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via \texttt{pip}. Source code, documentation, examples, benchmarks and tests may be found at \texttt{\url{https://github.com/patrick-kidger/signatory}}. The license is Apache-2.0. | https://openreview.net/pdf/21ae7cf194b2f80ef3d5a3fcfd985810e0982774.pdf |
Distance-Based Regularisation of Deep Networks for Fine-Tuning | https://openreview.net/forum?id=IFqrg1p5Bc | https://openreview.net/forum?id=IFqrg1p5Bc | Henry Gouk,Timothy Hospedales,massimiliano pontil | ICLR 2021,Poster | We investigate approaches to regularisation during fine-tuning of deep neural networks. First we provide a neural network generalisation bound based on Rademacher complexity that uses the distance the weights have moved from their initial values. This bound has no direct dependence on the number of weights and compares favourably to other bounds when applied to convolutional networks. Our bound is highly relevant for fine-tuning, because providing a network with a good initialisation based on transfer learning means that learning can modify the weights less, and hence achieve tighter generalisation. Inspired by this, we develop a simple yet effective fine-tuning algorithm that constrains the hypothesis class to a small sphere centred on the initial pre-trained weights, thus obtaining provably better generalisation performance than conventional transfer learning. Empirical evaluation shows that our algorithm works well, corroborating our theoretical results. It outperforms both state of the art fine-tuning competitors, and penalty-based alternatives that we show do not directly constrain the radius of the search space. | https://openreview.net/pdf/8758dc3fcea289b116ec58cc0b6feae810915b43.pdf |
Decoupling Global and Local Representations via Invertible Generative Flows | https://openreview.net/forum?id=iWLByfvUhN | https://openreview.net/forum?id=iWLByfvUhN | Xuezhe Ma,Xiang Kong,Shanghang Zhang,Eduard H Hovy | ICLR 2021,Poster | In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative flow in the VAE framework to model the decoder.
Specifically, the proposed model utilizes the variational auto-encoding framework to learn a (low-dimensional) vector of latent variables to capture the global information of an image, which is fed as a conditional input to a flow-based invertible decoder with architecture borrowed from style transfer literature.
Experimental results on standard image benchmarks demonstrate the effectiveness of our model in terms of density estimation, image generation and unsupervised representation learning.
Importantly, this work demonstrates that with only architectural inductive biases, a generative model with a likelihood-based objective is capable of learning decoupled representations, requiring no explicit supervision.
The code for our model is available at \url{https://github.com/XuezheMax/wolf}. | https://openreview.net/pdf/f2b3766907999d10ac5b2857ad661bce8495fe3c.pdf |
Understanding Over-parameterization in Generative Adversarial Networks | https://openreview.net/forum?id=C3qvk5IQIJY | https://openreview.net/forum?id=C3qvk5IQIJY | Yogesh Balaji,Mohammadmahdi Sajedi,Neha Mukund Kalibhat,Mucong Ding,Dominik Stöger,Mahdi Soltanolkotabi,Soheil Feizi | ICLR 2021,Poster | A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold. Indeed, most successful GANs used in practice are trained using overparameterized generator and discriminator networks, both in terms of depth and width. A large body of work in supervised learning have shown the importance of model overparameterization in the convergence of the gradient descent (GD) to globally optimal solutions. In contrast, the unsupervised setting and GANs in particular involve non-convex concave mini-max optimization problems that are often trained using Gradient Descent/Ascent (GDA).
The role and benefits of model overparameterization in the convergence of GDA to a global saddle point in non-convex concave problems is far less understood. In this work, we present a comprehensive analysis of the importance of model overparameterization in GANs both theoretically and empirically. We theoretically show that in an overparameterized GAN model with a $1$-layer neural network generator and a linear discriminator, GDA converges to a global saddle point of the underlying non-convex concave min-max problem. To the best of our knowledge, this is the first result for global convergence of GDA in such settings. Our theory is based on a more general result that holds for a broader class of nonlinear generators and discriminators that obey certain assumptions (including deeper generators and random feature discriminators). Our theory utilizes and builds upon a novel connection with the convergence analysis of linear time-varying dynamical systems which may have broader implications for understanding the convergence behavior of GDA for non-convex concave problems involving overparameterized models. We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets. Our experiments show that overparameterization improves the quality of generated samples across various model architectures and datasets. Remarkably, we observe that overparameterization leads to faster and more stable convergence behavior of GDA across the board. | https://openreview.net/pdf/c7a1dc846cfadfda171147b173c3155d0de4ef8a.pdf |
Filtered Inner Product Projection for Crosslingual Embedding Alignment | https://openreview.net/forum?id=A2gNouoXE7 | https://openreview.net/forum?id=A2gNouoXE7 | Vin Sachidananda,Ziyi Yang,Chenguang Zhu | ICLR 2021,Poster | Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, these algorithms have been studied in the setting of bilingual lexicon induction where one seeks to align the embeddings of a source and a target language such that translated word pairs lie close to one another in a common representation space. In this paper, we propose a method, Filtered Inner Product Projection (FIPP), for mapping embeddings to a common representation space. As semantic shifts are pervasive across languages and domains, FIPP first identifies the common geometric structure in both embeddings and then, only on the common structure, aligns the Gram matrices of these embeddings. FIPP is applicable even when the source and target embeddings are of differing dimensionalities. Additionally, FIPP provides computational benefits in ease of implementation and is faster to compute than current approaches. Following the baselines in Glavas et al. 2019, we evaluate FIPP both in the context of bilingual lexicon induction and downstream language tasks. We show that FIPP outperforms existing methods on the XLING BLI dataset for most language pairs while also providing robust performance across downstream tasks. | https://openreview.net/pdf/2638533f1265729a64b0eaf1d56f4ca348fd18dc.pdf |
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks | https://openreview.net/forum?id=YUGG2tFuPM | https://openreview.net/forum?id=YUGG2tFuPM | Alexander Levine,Soheil Feizi | ICLR 2021,Poster | Adversarial poisoning attacks distort training data in order to corrupt the test-time behavior of a classifier. A provable defense provides a certificate for each test sample, which is a lower bound on the magnitude of any adversarial distortion of the training set that can corrupt the test sample's classification.
We propose two novel provable defenses against poisoning attacks: (i) Deep Partition Aggregation (DPA), a certified defense against a general poisoning threat model, defined as the insertion or deletion of a bounded number of samples to the training set --- by implication, this threat model also includes arbitrary distortions to a bounded number of images and/or labels; and (ii) Semi-Supervised DPA (SS-DPA), a certified defense against label-flipping poisoning attacks. DPA is an ensemble method where base models are trained on partitions of the training set determined by a hash function. DPA is related to both subset aggregation, a well-studied ensemble method in classical machine learning, as well as to randomized smoothing, a popular provable defense against evasion (inference) attacks. Our defense against label-flipping poison attacks, SS-DPA, uses a semi-supervised learning algorithm as its base classifier model: each base classifier is trained using the entire unlabeled training set in addition to the labels for a partition. SS-DPA significantly outperforms the existing certified defense for label-flipping attacks (Rosenfeld et al., 2020) on both MNIST and CIFAR-10: provably tolerating, for at least half of test images, over 600 label flips (vs. < 200 label flips) on MNIST and over 300 label flips (vs. 175 label flips) on CIFAR-10. Against general poisoning attacks where no prior certified defenses exists, DPA can certify $\geq$ 50% of test images against over 500 poison image insertions on MNIST, and nine insertions on CIFAR-10. These results establish new state-of-the-art provable defenses against general and label-flipping poison attacks. Code is available at https://github.com/alevine0/DPA | https://openreview.net/pdf/d7ef1d2223ddeebc34ef648c6db9ba088ba871bb.pdf |
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents | https://openreview.net/forum?id=9GBZBPn0Jx | https://openreview.net/forum?id=9GBZBPn0Jx | Deyao Zhu,Mohamed Zahran,Li Erran Li,Mohamed Elhoseiny | ICLR 2021,Poster | Motion forecasting is essential for making intelligent decisions in robotic navigation. As a result, the multi-agent behavioral prediction has become a core component of modern human-robot interaction applications such as autonomous driving. Due to various intentions and interactions among agents, agent trajectories can have multiple possible futures. Hence, the motion forecasting model's ability to cover possible modes becomes essential to enable accurate prediction. Towards this goal, we introduce HalentNet to better model the future motion distribution in addition to a traditional trajectory regression learning objective by incorporating generative augmentation losses. We model intents with unsupervised discrete random variables whose training is guided by a collaboration between two key signals: A discriminative loss that encourages intents' diversity and a hallucinative loss that explores intent transitions (i.e., mixed intents) and encourages their smoothness. This regulates the neural network behavior to be more accurately predictive on uncertain scenarios due to the active yet careful exploration of possible future agent behavior. Our model's learned representation leads to better and more semantically meaningful coverage of the trajectory distribution. Our experiments show that our method can improve over the state-of-the-art trajectory forecasting benchmarks, including vehicles and pedestrians, for about 20% on average FDE and 50% on road boundary violation rate when predicting 6 seconds future. We also conducted human experiments to show that our predicted trajectories received 39.6% more votes than the runner-up approach and 32.2% more votes than our variant without hallucinative mixed intent loss. The code will be released soon. | https://openreview.net/pdf/9f7a72a2ef90cdbf07b3067bf2417e59741a2483.pdf |
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving | https://openreview.net/forum?id=O6LPudowNQm | https://openreview.net/forum?id=O6LPudowNQm | Yuhuai Wu,Albert Jiang,Jimmy Ba,Roger Baker Grosse | ICLR 2021,Poster | In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark designed to test agents’ generalization ability. INT is based on a theorem generator, which provides theoretically infinite data and allows us to measure 6 different types of generalization, each reflecting a distinct challenge, characteristic of automated theorem proving. In addition, provides a fast theorem proving environment with sequence-based and graph-based interfaces, conducive to performing learning-based research. We introduce base-lines with architectures including transformers and graph neural networks (GNNs)for INT. Using INT, we find that transformer-based agents achieve stronger test performance for most of the generalization tasks, despite having much larger out-of-distribution generalization gaps than GNNs. We further find that the addition of Monte Carlo Tree Search (MCTS) at test time helps to prove new theorems. | https://openreview.net/pdf/bf947d0003a7bfa73f42739223859badb472d6d5.pdf |
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes | https://openreview.net/forum?id=lgNx56yZh8a | https://openreview.net/forum?id=lgNx56yZh8a | Jake Snell,Richard Zemel | ICLR 2021,Poster | Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitioners to specify prior beliefs and update those beliefs in light of observed data. Contemporary approaches to Bayesian few-shot classification maintain a posterior distribution over model parameters, which is slow and requires storage that scales with model size. Instead, we propose a Gaussian process classifier based on a novel combination of Pólya-Gamma augmentation and the one-vs-each softmax approximation that allows us to efficiently marginalize over functions rather than model parameters. We demonstrate improved accuracy and uncertainty quantification on both standard few-shot classification benchmarks and few-shot domain transfer tasks. | https://openreview.net/pdf/7b95dc29c35f2fff01beeecbf82b977b59478341.pdf |
A Hypergradient Approach to Robust Regression without Correspondence | https://openreview.net/forum?id=l35SB-_raSQ | https://openreview.net/forum?id=l35SB-_raSQ | Yujia Xie,Yixiu Mao,Simiao Zuo,Hongteng Xu,Xiaojing Ye,Tuo Zhao,Hongyuan Zha | ICLR 2021,Poster | We consider a regression problem, where the correspondence between the input and output data is not available. Such shuffled data are commonly observed in many real world problems. Take flow cytometry as an example: the measuring instruments are unable to preserve the correspondence between the samples and the measurements. Due to the combinatorial nature of the problem, most of the existing methods are only applicable when the sample size is small, and are limited to linear regression models. To overcome such bottlenecks, we propose a new computational framework --- ROBOT --- for the shuffled regression problem, which is applicable to large data and complex models. Specifically, we propose to formulate regression without correspondence as a continuous optimization problem. Then by exploiting the interaction between the regression model and the data correspondence, we propose to develop a hypergradient approach based on differentiable programming techniques. Such a hypergradient approach essentially views the data correspondence as an operator of the regression model, and therefore it allows us to find a better descent direction for the model parameters by differentiating through the data correspondence. ROBOT is quite general, and can be further extended to an inexact correspondence setting, where the input and output data are not necessarily exactly aligned. Thorough numerical experiments show that ROBOT achieves better performance than existing methods in both linear and nonlinear regression tasks, including real-world applications such as flow cytometry and multi-object tracking. | https://openreview.net/pdf/01f4f60af211bcbeb9691824fbd8f92211e50098.pdf |
C-Learning: Horizon-Aware Cumulative Accessibility Estimation | https://openreview.net/forum?id=W3Wf_wKmqm9 | https://openreview.net/forum?id=W3Wf_wKmqm9 | Panteha Naderian,Gabriel Loaiza-Ganem,Harry J. Braviner,Anthony L. Caterini,Jesse C. Cresswell,Tong Li,Animesh Garg | ICLR 2021,Poster | Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex motion planning tasks. In order to address these limitations, we introduce the concept of cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that these functions obey a recurrence relation, which enables learning from offline interactions. We also prove that optimal cumulative accessibility functions are monotonic in the planning horizon. Additionally, our method can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We evaluate our approach on a set of multi-goal discrete and continuous control tasks. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality. Our code is available at https://github.com/layer6ai-labs/CAE, and additional visualizations can be found at https://sites.google.com/view/learning-cae/. | https://openreview.net/pdf/a50d45f24c305299cc3ac8aff9cd1d83ec4861e5.pdf |
MIROSTAT: A NEURAL TEXT DECODING ALGORITHM THAT DIRECTLY CONTROLS PERPLEXITY | https://openreview.net/forum?id=W1G1JZEIy5_ | https://openreview.net/forum?id=W1G1JZEIy5_ | Sourya Basu,Govardana Sachitanandam Ramachandran,Nitish Shirish Keskar,Lav R. Varshney | ICLR 2021,Poster | Neural text decoding algorithms strongly influence the quality of texts generated using language models, but popular algorithms like top-k, top-p (nucleus), and temperature-based sampling may yield texts that have objectionable repetition or incoherence. Although these methods generate high-quality text after ad hoc parameter tuning that depends on the language model and the length of generated text, not much is known about the control they provide over the statistics of the output. This is important, however, since recent reports show that humans prefer when perplexity is neither too much nor too little and since we experimentally show that cross-entropy (log of perplexity) has a near-linear relation with repetition. First, we provide a theoretical analysis of perplexity in top-k, top-p, and temperature sampling, under Zipfian statistics. Then, we use this analysis to design a feedback-based adaptive top-k text decoding algorithm called mirostat that generates text (of any length) with a predetermined target value of perplexity without any tuning. Experiments show that for low values of k and p, perplexity drops significantly with generated text length and leads to excessive repetitions (the boredom trap). Contrarily, for large values of k and p, perplexity increases with generated text length and leads to incoherence (confusion trap). Mirostat avoids both traps. Specifically, we show that setting target perplexity value beyond a threshold yields negligible sentence-level repetitions. Experiments with
human raters for fluency, coherence, and quality further verify our findings. | https://openreview.net/pdf/8e680c2eecb056aeaf306b71235d6dc6748d9d33.pdf |
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime | https://openreview.net/forum?id=bB2drc7DPuB | https://openreview.net/forum?id=bB2drc7DPuB | Andrea Agazzi,Jianfeng Lu | ICLR 2021,Poster | We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g. the behavior of wide single hidden layer neural networks, when exploration is encouraged through entropy regularization. The dynamics of these models is established as a Wasserstein gradient flow of distributions in parameter space. We further prove global optimality of the fixed points of this dynamics under mild conditions on their initialization. | https://openreview.net/pdf/17d9d92e96c6ed3bfb2eeaec3ea3cfacdcbfc029.pdf |
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers | https://openreview.net/forum?id=guetrIHLFGI | https://openreview.net/forum?id=guetrIHLFGI | Preetum Nakkiran,Behnam Neyshabur,Hanie Sedghi | ICLR 2021,Poster | We propose a new framework for reasoning about generalization in deep learning.
The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error into: (1) the Ideal World test error plus (2) the gap between the two worlds. If the gap (2) is universally small, this reduces the problem of generalization in offline learning to the problem of optimization in online learning.
We then give empirical evidence that this gap between worlds can be small in realistic deep learning settings, in particular supervised image classification. For example, CNNs generalize better than MLPs on image distributions in the Real World, but this is "because" they optimize faster on the population loss in the Ideal World. This suggests our framework is a useful tool for understanding generalization in deep learning, and lays the foundation for future research in this direction. | https://openreview.net/pdf/04581f1e8f49dff9317ee5405d413f8b8a245a82.pdf |
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning | https://openreview.net/forum?id=vYVI1CHPaQg | https://openreview.net/forum?id=vYVI1CHPaQg | Samuel Horváth,Peter Richtarik | ICLR 2021,Poster | Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computing systems. A key bottleneck of such systems is the communication overhead for exchanging information across the workers, such as stochastic gradients. Among the many techniques proposed to remedy this issue, one of the most successful is the framework of compressed communication with error feedback (EF). EF remains the only known technique that can deal with the error induced by contractive compressors which are not unbiased, such as Top-$K$ or PowerSGD. In this paper, we propose a new and theoretically and practically better alternative to EF for dealing with contractive compressors. In particular, we propose a construction which can transform any contractive compressor into an induced unbiased compressor. Following this transformation, existing methods able to work with unbiased compressors can be applied. We show that our approach leads to vast improvements over EF, including reduced memory requirements, better communication complexity guarantees and fewer assumptions. We further extend our results to federated learning with partial participation following an arbitrary distribution over the nodes and demonstrate the benefits thereof. We perform several numerical experiments which validate our theoretical findings. | https://openreview.net/pdf/74506361fdee66a12846aa4de625aa16b5745878.pdf |
DrNAS: Dirichlet Neural Architecture Search | https://openreview.net/forum?id=9FWas6YbmB3 | https://openreview.net/forum?id=9FWas6YbmB3 | Xiangning Chen,Ruochen Wang,Minhao Cheng,Xiaocheng Tang,Cho-Jui Hsieh | ICLR 2021,Poster | This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing weight as random variables, modeled by Dirichlet distribution. With recently developed pathwise derivatives, the Dirichlet parameters can be easily optimized with gradient-based optimizer in an end-to-end manner. This formulation improves the generalization ability and induces stochasticity that naturally encourages exploration in the search space. Furthermore, to alleviate the large memory consumption of differentiable NAS, we propose a simple yet effective progressive learning scheme that enables searching directly on large-scale tasks, eliminating the gap between search and evaluation phases. Extensive experiments demonstrate the effectiveness of our method. Specifically, we obtain a test error of 2.46\% for CIFAR-10, 23.7\% for ImageNet under the mobile setting. On NAS-Bench-201, we also achieve state-of-the-art results on all three datasets and provide insights for the effective design of neural architecture search algorithms. | https://openreview.net/pdf/3203d1c8e155920800406d00c89682d213d4a60e.pdf |
Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation | https://openreview.net/forum?id=FmMKSO4e8JK | https://openreview.net/forum?id=FmMKSO4e8JK | Justin Fu,Sergey Levine | ICLR 2021,Poster | In this work we consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectures. Because the available data typically only covers a small manifold of the possible space of inputs, a principal challenge is to be able to construct algorithms that can reason about uncertainty and out-of-distribution values, since a naive optimizer can easily exploit an estimated model to return adversarial inputs. We propose to tackle the MBO problem by leveraging the normalized maximum-likelihood (NML) estimator, which provides a principled approach to handling uncertainty and out-of-distribution inputs. While in the standard formulation NML is intractable, we propose a tractable approximation that allows us to scale our method to high-capacity neural network models. We demonstrate that our method can effectively optimize high-dimensional design problems in a variety of disciplines such as chemistry, biology, and materials engineering. | https://openreview.net/pdf/4fc7d9a4093bc9bafddf46785cafda74e0be40ce.pdf |
Viewmaker Networks: Learning Views for Unsupervised Representation Learning | https://openreview.net/forum?id=enoVQWLsfyL | https://openreview.net/forum?id=enoVQWLsfyL | Alex Tamkin,Mike Wu,Noah Goodman | ICLR 2021,Poster | Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input. However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across domains and modalities. To address this, we propose viewmaker networks: generative models that learn to produce useful views from a given input. Viewmakers are stochastic bounded adversaries: they produce views by generating and then adding an $\ell_p$-bounded perturbation to the input, and are trained adversarially with respect to the main encoder network. Remarkably, when pretraining on CIFAR-10, our learned views enable comparable transfer accuracy to the well-tuned SimCLR augmentations---despite not including transformations like cropping or color jitter. Furthermore, our learned views significantly outperform baseline augmentations on speech recordings (+9 points on average) and wearable sensor data (+17 points on average). Viewmaker views can also be combined with handcrafted views: they improve robustness to common image corruptions and can increase transfer performance in cases where handcrafted views are less explored. These results suggest that viewmakers may provide a path towards more general representation learning algorithms---reducing the domain expertise and effort needed to pretrain on a much wider set of domains. Code is available at https://github.com/alextamkin/viewmaker. | https://openreview.net/pdf/c5ab1b6ae923a3e1807e6a3b96739798c0021c90.pdf |
Robust Pruning at Initialization | https://openreview.net/forum?id=vXj_ucZQ4hA | https://openreview.net/forum?id=vXj_ucZQ4hA | Soufiane Hayou,Jean-Francois Ton,Arnaud Doucet,Yee Whye Teh | ICLR 2021,Poster | Overparameterized Neural Networks (NN) display state-of-the-art performance. However, there is a growing need for smaller, energy-efficient, neural networks to be able to use machine learning applications on devices with limited computational resources. A popular approach consists of using pruning techniques. While these techniques have traditionally focused on pruning pre-trained NN (LeCun et al.,1990; Hassibi et al., 1993), recent work by Lee et al. (2018) has shown promising results when pruning at initialization. However, for Deep NNs, such procedures remain unsatisfactory as the resulting pruned networks can be difficult to train and, for instance, they do not prevent one layer from being fully pruned. In this paper, we provide a comprehensive theoretical analysis of Magnitude and Gradient based pruning at initialization and training of sparse architectures. This allows us to propose novel principled approaches which we validate experimentally on a variety of NN architectures. | https://openreview.net/pdf/2e8257a71504620d9109ae3b12c4c0c228f26db0.pdf |
Single-Photon Image Classification | https://openreview.net/forum?id=CHLhSw9pSw8 | https://openreview.net/forum?id=CHLhSw9pSw8 | Thomas Fischbacher,Luciano Sbaiz | ICLR 2021,Poster | Quantum Computing based Machine Learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature. We demonstrate the existence of a "quantum computing toy model" that illustrates key aspects of quantum information processing while being experimentally accessible with room temperature optics. Pondering the question of the theoretical classification accuracy performance limit for MNIST (respectively "Fashion-MNIST") classifiers, subject to the constraint that a decision has to be made after detection of the very first photon that passed through an image-filter, we show that a machine learning system that is permitted to use quantum interference on the photon's state can substantially outperform any machine learning system that can not. Specifically, we prove that a "classical" MNIST (respectively "Fashion-MNIST") classifier cannot achieve an accuracy of better than $21.28\%$ (respectively $18.28\%$ for "Fashion-MNIST") if it must make a decision after seeing a single photon falling on one of the $28\times 28$ image pixels of a detector array. We further demonstrate that a classifier that is permitted to employ quantum interference by optically transforming the photon state prior to detection can achieve a classification accuracy of at least $41.27\%$ for MNIST (respectively $36.14\%$ for "Fashion-MNIST"). We show in detail how to train the corresponding quantum state transformation with TensorFlow and also explain how this example can serve as a teaching tool for the measurement process in quantum mechanics.
| https://openreview.net/pdf/d7978e041131e45cff5b7c28261bcb70fb3b1655.pdf |
Can a Fruit Fly Learn Word Embeddings? | https://openreview.net/forum?id=xfmSoxdxFCG | https://openreview.net/forum?id=xfmSoxdxFCG | Yuchen Liang,Chaitanya Ryali,Benjamin Hoover,Leopold Grinberg,Saket Navlakha,Mohammed J Zaki,Dmitry Krotov | ICLR 2021,Poster | The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representation of the inputs. In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task. We show that this network can learn semantic representations of words and can generate both static and context-dependent word embeddings. Unlike conventional methods (e.g., BERT, GloVe) that use dense representations for word embedding, our algorithm encodes semantic meaning of words and their context in the form of sparse binary hash codes. The quality of the learned representations is evaluated on word similarity analysis, word-sense disambiguation, and document classification. It is shown that not only can the fruit fly network motif achieve performance comparable to existing methods in NLP, but, additionally, it uses only a fraction of the computational resources (shorter training time and smaller memory footprint). | https://openreview.net/pdf/5c9eaf676c55218a86df8d8970d3ee1d9021686c.pdf |
A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels | https://openreview.net/forum?id=ajOrOhQOsYx | https://openreview.net/forum?id=ajOrOhQOsYx | Leon Lang,Maurice Weiler | ICLR 2021,Poster | Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance. Recent advances in the theoretical description of GCNNs revealed that such models can generally be understood as performing convolutions with $G$-steerable kernels, that is, kernels that satisfy an equivariance constraint themselves. While the $G$-steerability constraint has been derived, it has to date only been solved for specific use cases - a general characterization of $G$-steerable kernel spaces is still missing. This work provides such a characterization for the practically relevant case of $G$ being any compact group. Our investigation is motivated by a striking analogy between the constraints underlying steerable kernels on the one hand and spherical tensor operators from quantum mechanics on the other hand. By generalizing the famous Wigner-Eckart theorem for spherical tensor operators, we prove that steerable kernel spaces are fully understood and parameterized in terms of 1) generalized reduced matrix elements, 2) Clebsch-Gordan coefficients, and 3) harmonic basis functions on homogeneous spaces. | https://openreview.net/pdf/2b834ab8bd8e5862e1cbaac32cff3d6594975b30.pdf |
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds | https://openreview.net/forum?id=dKg5D1Z1Lm | https://openreview.net/forum?id=dKg5D1Z1Lm | Yihao Feng,Ziyang Tang,na zhang,qiang liu | ICLR 2021,Poster | Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data collection is expensive or even unsafe. As the observed data tends to be noisy and limited, it is essential to provide rigorous uncertainty quantification, not just a point estimation, when applying OPE to make high stakes decisions. This work considers the problem of constructing non-asymptotic confidence intervals in infinite-horizon off-policy evaluation, which remains a challenging open question. We develop a practical algorithm through a primal-dual optimization-based approach, which leverages the kernel Bellman loss (KBL) of Feng et al. 2019 and a new martingale concentration inequality of KBL applicable to time-dependent data with unknown mixing conditions. Our algorithm makes minimum assumptions on the data and the function class of the Q-function, and works for the behavior-agnostic settings where the data is collected under a mix of arbitrary unknown behavior policies. We present empirical results that clearly demonstrate the advantages of our approach over existing methods.
| https://openreview.net/pdf/05280659a19a8436c51ea6a8b704610bdf16c3e9.pdf |
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation | https://openreview.net/forum?id=9YlaeLfuhJF | https://openreview.net/forum?id=9YlaeLfuhJF | Karan Goel,Albert Gu,Yixuan Li,Christopher Re | ICLR 2021,Poster | Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage. To mitigate these performance differences, we introduce model patching, a two-stage framework for improving robustness that encourages the model to be invariant to subgroup differences, and focus on class information shared by subgroups. Model patching first models subgroup features within a class and learns semantic transformations between them, and then trains a classifier with data augmentations that deliberately manipulate subgroup features. We instantiate model patching with CAMEL, which (1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and (2) balances subgroup performance using a theoretically-motivated subgroup consistency regularizer, accompanied by a new robust objective. We demonstrate CAMEL’s effectiveness on 3 benchmark datasets, with reductions in robust error of up to 33% relative to the best baseline. Lastly, CAMEL successfully patches a model that fails due to spurious features on a real-world skin cancer dataset. | https://openreview.net/pdf/a78017923e9af1b5c37e2ec56d3564794b91dd79.pdf |
SOLAR: Sparse Orthogonal Learned and Random Embeddings | https://openreview.net/forum?id=fw-BHZ1KjxJ | https://openreview.net/forum?id=fw-BHZ1KjxJ | Tharun Medini,Beidi Chen,Anshumali Shrivastava | ICLR 2021,Poster | Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. However, the bottleneck of indexing a large number of dense vectors and performing an NNS hurts the query time and accuracy of these models. In this paper, we argue that high-dimensional and ultra-sparse embedding is a significantly superior alternative to dense low-dimensional embedding for both query efficiency and accuracy. Extreme sparsity eliminates the need for NNS by replacing them with simple lookups, while its high dimensionality ensures that the embeddings are informative even when sparse. However, learning extremely high dimensional embeddings leads to blow up in the model size. To make the training feasible, we propose a partitioning algorithm that learns such high dimensional embeddings across multiple GPUs without any communication. This is facilitated by our novel asymmetric mixture of Sparse, Orthogonal, Learned and Random (SOLAR) Embeddings. The label vectors are random, sparse, and near-orthogonal by design, while the query vectors are learned and sparse. We theoretically prove that our way of one-sided learning is equivalent to learning both query and label embeddings. With these unique properties, we can successfully train 500K dimensional SOLAR embeddings for the tasks of searching through 1.6M books and multi-label classification on the three largest public datasets. We achieve superior precision and recall compared to the respective state-of-the-art baselines for each task with up to 10 times faster speed. | https://openreview.net/pdf/b5e7f77ff9194315708e327c8be7e9ff929d902a.pdf |
Neural representation and generation for RNA secondary structures | https://openreview.net/forum?id=snOgiCYZgJ7 | https://openreview.net/forum?id=snOgiCYZgJ7 | Zichao Yan,William L. Hamilton,Mathieu Blanchette | ICLR 2021,Poster | Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques, which represents a key but underappreciated aspect of computational drug discovery. In this work, we investigate the principles behind representing and generating different RNA structural modalities, and propose a flexible framework to jointly embed and generate these molecular structures along with their sequence in a meaningful latent space. Equipped with a deep understanding of RNA molecular structures, our most sophisticated encoding and decoding methods operate on the molecular graph as well as the junction tree hierarchy, integrating strong inductive bias about RNA structural regularity and folding mechanism such that high structural validity, stability and diversity of generated RNAs are achieved. Also, we seek to adequately organize the latent space of RNA molecular embeddings with regard to the interaction with proteins, and targeted optimization is used to navigate in this latent space to search for desired novel RNA molecules. | https://openreview.net/pdf/bc9ac721f355bf79861be2019a3d66689b2b8a33.pdf |
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks | https://openreview.net/forum?id=vVjIW3sEc1s | https://openreview.net/forum?id=vVjIW3sEc1s | Nikunj Saunshi,Sadhika Malladi,Sanjeev Arora | ICLR 2021,Poster | Autoregressive language models, pretrained using large text corpora to do well on next word prediction, have been successful at solving many downstream tasks, even with zero-shot usage. However, there is little theoretical understanding of this success. This paper initiates a mathematical study of this phenomenon for the downstream task of text classification by considering the following questions: (1) What is the intuitive connection between the pretraining task of next word prediction and text classification? (2) How can we mathematically formalize this connection and quantify the benefit of language modeling? For (1), we hypothesize, and verify empirically, that classification tasks of interest can be reformulated as sentence completion tasks, thus making language modeling a meaningful pretraining task. With a mathematical formalization of this hypothesis, we make progress towards (2) and show that language models that are $\epsilon$-optimal in cross-entropy (log-perplexity) learn features that can linearly solve such classification tasks with $\mathcal{O}(\sqrt{\epsilon})$ error, thus demonstrating that doing well on language modeling can be beneficial for downstream tasks. We experimentally verify various assumptions and theoretical findings, and also use insights from the analysis to design a new objective function that performs well on some classification tasks. | https://openreview.net/pdf/b890b55e177e79485185ef1fb9eb4e50d981908a.pdf |
Noise or Signal: The Role of Image Backgrounds in Object Recognition | https://openreview.net/forum?id=gl3D-xY7wLq | https://openreview.net/forum?id=gl3D-xY7wLq | Kai Yuanqing Xiao,Logan Engstrom,Andrew Ilyas,Aleksander Madry | ICLR 2021,Poster | We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 88% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
| https://openreview.net/pdf/16106e84acc8eb97c1a091b0c318d22052d9ab9b.pdf |
Does enhanced shape bias improve neural network robustness to common corruptions? | https://openreview.net/forum?id=yUxUNaj2Sl | https://openreview.net/forum?id=yUxUNaj2Sl | Chaithanya Kumar Mummadi,Ranjitha Subramaniam,Robin Hutmacher,Julien Vitay,Volker Fischer,Jan Hendrik Metzen | ICLR 2021,Poster | Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen test data from the same distribution as the training data but often fail to generalize to out-of-distribution data. It has been shown that augmenting the training data with different image styles decreases this texture bias in favor of increased shape bias while at the same time improving robustness to common corruptions, such as noise and blur. Commonly, this is interpreted as shape bias increasing corruption robustness. However, this relationship is only hypothesized. We perform a systematic study of different ways of composing inputs based on natural images, explicit edge information, and stylization. While stylization is essential for achieving high corruption robustness, we do not find a clear correlation between shape bias and robustness. We conclude that the data augmentation caused by style-variation accounts for the improved corruption robustness and increased shape bias is only a byproduct. | https://openreview.net/pdf/afd34372762e3e8c1a3e742ee5fa8ae88e866c47.pdf |
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning | https://openreview.net/forum?id=LXMSvPmsm0g | https://openreview.net/forum?id=LXMSvPmsm0g | Tianlong Chen,Zhenyu Zhang,Sijia Liu,Shiyu Chang,Zhangyang Wang | ICLR 2021,Poster | The lottery ticket hypothesis states that a highly sparsified sub-network can be trained in isolation, given the appropriate weight initialization. This paper extends that hypothesis from one-shot task learning, and demonstrates for the first time that such extremely compact and independently trainable sub-networks can be also identified in the lifelong learning scenario, which we call lifelong tickets. We show that the resulting lifelong ticket can further be leveraged to improve the performance of learning over continual tasks. However, it is highly non-trivial to conduct network pruning in the lifelong setting. Two critical roadblocks arise: i) As many tasks now arrive sequentially, finding tickets in a greedy weight pruning fashion will inevitably suffer from the intrinsic bias, that the earlier emerging tasks impact more; ii) As lifelong learning is consistently challenged by catastrophic forgetting, the compact network capacity of tickets might amplify the risk of forgetting. In view of those, we introduce two pruning options, e.g., top-down and bottom-up, for finding lifelong tickets. Compared to the top-down pruning that extends vanilla (iterative) pruning over sequential tasks, we show that the bottom-up one, which can dynamically shrink and (re-)expand model capacity, effectively avoids the undesirable excessive pruning in the early stage. We additionally introduce lottery teaching that further overcomes forgetting via knowledge distillation aided by external unlabeled data. Unifying those ingredients, we demonstrate the existence of very competitive lifelong tickets, e.g., achieving 3-8% of the dense model size with even higher accuracy, compared to strong class-incremental learning baselines on CIFAR-10/CIFAR-100/Tiny-ImageNet datasets. Codes available at https://github.com/VITA-Group/Lifelong-Learning-LTH. | https://openreview.net/pdf/b8a21c081c59f4808478ea25d46d2743c4d51298.pdf |
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit | https://openreview.net/forum?id=MjvduJCsE4 | https://openreview.net/forum?id=MjvduJCsE4 | Ben Adlam,Jaehoon Lee,Lechao Xiao,Jeffrey Pennington,Jasper Snoek | ICLR 2021,Poster | Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor uncertainty estimates, such as overconfidence on out-of-distribution (OOD) data and ungraceful failing under distributional shift. Previous benchmarks have found that ensembles of neural networks (NNs) are typically the best calibrated models on OOD data. Inspired by this, we leverage recent theoretical advances that characterize the function-space prior of an infinitely-wide NN as a Gaussian process, termed the neural network Gaussian process (NNGP). We use the NNGP with a softmax link function to build a probabilistic model for multi-class classification and marginalize over the latent Gaussian outputs to sample from the posterior. This gives us a better understanding of the implicit prior NNs place on function space and allows a direct comparison of the calibration of the NNGP and its finite-width analogue. We also examine the calibration of previous approaches to classification with the NNGP, which treat classification problems as regression to the one-hot labels. In this case the Bayesian posterior is exact, and we compare several heuristics to generate a categorical distribution over classes. We find these methods are well calibrated under distributional shift. Finally, we consider an infinite-width final layer in conjunction with a pre-trained embedding. This replicates the important practical use case of transfer learning and allows scaling to significantly larger datasets. As well as achieving competitive predictive accuracy, this approach is better calibrated than its finite width analogue. | https://openreview.net/pdf/bbfc60f0ff2a8b621963be0d68a713c42e879919.pdf |
Hierarchical Autoregressive Modeling for Neural Video Compression | https://openreview.net/forum?id=TK_6nNb_C7q | https://openreview.net/forum?id=TK_6nNb_C7q | Ruihan Yang,Yibo Yang,Joseph Marino,Stephan Mandt | ICLR 2021,Poster | Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neural video compression methods (Lu et al., 2019; Yang et al., 2020b; Agustssonet al., 2020) as instances of a generalized stochastic temporal autoregressive transform, and propose avenues for enhancement based on this insight. Comprehensive evaluations on large-scale video data show improved rate-distortion performance over both state-of-the-art neural and conventional video compression methods. | https://openreview.net/pdf/aa19952024b8087f98c378025770cfb48b752442.pdf |
DeLighT: Deep and Light-weight Transformer | https://openreview.net/forum?id=ujmgfuxSLrO | https://openreview.net/forum?id=ujmgfuxSLrO | Sachin Mehta,Marjan Ghazvininejad,Srinivasan Iyer,Luke Zettlemoyer,Hannaneh Hajishirzi | ICLR 2021,Poster | We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block using the DeLighT transformation, a deep and light-weight transformation and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output. Overall, DeLighT networks are 2.5 to 4 times deeper than standard transformer models and yet have fewer parameters and operations. Experiments on benchmark machine translation and language modeling tasks show that DeLighT matches or improves the performance of baseline Transformers with 2 to 3 times fewer parameters on average. | https://openreview.net/pdf/4f21fde99bd3a5d443e49b301eed13de7442a1a6.pdf |
Learning A Minimax Optimizer: A Pilot Study | https://openreview.net/forum?id=nkIDwI6oO4_ | https://openreview.net/forum?id=nkIDwI6oO4_ | Jiayi Shen,Xiaohan Chen,Howard Heaton,Tianlong Chen,Jialin Liu,Wotao Yin,Zhangyang Wang | ICLR 2021,Poster | Solving continuous minimax optimization is of extensive practical interest, yet notoriously unstable and difficult. This paper introduces the learning to optimize(L2O) methodology to the minimax problems for the first time and addresses its accompanying unique challenges. We first present Twin-L2O, the first dedicated minimax L2O method consisting of two LSTMs for updating min and max variables separately. The decoupled design is found to facilitate learning, particularly when the min and max variables are highly asymmetric. Empirical experiments on a variety of minimax problems corroborate the effectiveness of Twin-L2O. We then discuss a crucial concern of Twin-L2O, i.e., its inevitably limited generalizability to unseen optimizees. To address this issue, we present two complementary strategies. Our first solution, Enhanced Twin-L2O, is empirically applicable for general minimax problems, by improving L2O training via leveraging curriculum learning. Our second alternative, called Safeguarded Twin-L2O, is a preliminary theoretical exploration stating that under some strong assumptions, it is possible to theoretically establish the convergence of Twin-L2O. We benchmark our algorithms on several testbed problems and compare against state-of-the-art minimax solvers. The code is available at: https://github.com/VITA-Group/L2O-Minimax. | https://openreview.net/pdf/d13003c8f7a704dcb012e4a100a51df5332b5286.pdf |
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online | https://openreview.net/forum?id=zElset1Klrp | https://openreview.net/forum?id=zElset1Klrp | Yangchen Pan,Kirby Banman,Martha White | ICLR 2021,Poster | Recent work has shown that sparse representations---where only a small percentage of units are active---can significantly reduce interference. Those works, however, relied on relatively complex regularization or meta-learning approaches, that have only been used offline in a pre-training phase. In this work, we pursue a direction that achieves sparsity by design, rather than by learning. Specifically, we design an activation function that produces sparse representations deterministically by construction, and so is more amenable to online training. The idea relies on the simple approach of binning, but overcomes the two key limitations of binning: zero gradients for the flat regions almost everywhere, and lost precision---reduced discrimination---due to coarse aggregation. We introduce a Fuzzy Tiling Activation (FTA) that provides non-negligible gradients and produces overlap between bins that improves discrimination. We first show that FTA is robust under covariate shift in a synthetic online supervised learning problem, where we can vary the level of correlation and drift. Then we move to the deep reinforcement learning setting and investigate both value-based and policy gradient algorithms that use neural networks with FTAs, in classic discrete control and Mujoco continuous control environments. We show that algorithms equipped with FTAs are able to learn a stable policy faster without needing target networks on most domains. | https://openreview.net/pdf/37f746394b25e91e61274da8ebafc304ae3d32a0.pdf |
FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | https://openreview.net/forum?id=dgtpE6gKjHn | https://openreview.net/forum?id=dgtpE6gKjHn | Hong-You Chen,Wei-Lun Chao | ICLR 2021,Poster | Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate local models into a global model, which has been shown challenging when users have non-i.i.d. data. In this paper, we propose a novel aggregation algorithm named FedBE, which takes a Bayesian inference perspective by sampling higher-quality global models and combining them via Bayesian model Ensemble, leading to much robust aggregation. We show that an effective model distribution can be constructed by simply fitting a Gaussian or Dirichlet distribution to the local models. Our empirical studies validate FedBE's superior performance, especially when users' data are not i.i.d. and when the neural networks go deeper. Moreover, FedBE is compatible with recent efforts in regularizing users' model training, making it an easily applicable module: you only need to replace the aggregation method but leave other parts of your federated learning algorithm intact. | https://openreview.net/pdf/9ba5f8c61a25604e0dd3f88e6c9d54997bdad777.pdf |
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | https://openreview.net/forum?id=7uVcpu-gMD | https://openreview.net/forum?id=7uVcpu-gMD | Róbert Csordás,Sjoerd van Steenkiste,Jürgen Schmidhuber | ICLR 2021,Poster | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks. | https://openreview.net/pdf/c1cdbcd3fd43a12f6a997140406b6771945a251b.pdf |
Calibration tests beyond classification | https://openreview.net/forum?id=-bxf89v3Nx | https://openreview.net/forum?id=-bxf89v3Nx | David Widmann,Fredrik Lindsten,Dave Zachariah | ICLR 2021,Poster | Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point estimates. Such models can be a valuable tool in decision-making under uncertainty, provided that the model output is meaningful and interpretable. Calibrated models guarantee that the probabilistic predictions are neither over- nor under-confident. In the machine learning literature, different measures and statistical tests have been proposed and studied for evaluating the calibration of classification models. For regression problems, however, research has been focused on a weaker condition of calibration based on predicted quantiles for real-valued targets. In this paper, we propose the first framework that unifies calibration evaluation and tests for general probabilistic predictive models. It applies to any such model, including classification and regression models of arbitrary dimension. Furthermore, the framework generalizes existing measures and provides a more intuitive reformulation of a recently proposed framework for calibration in multi-class classification. In particular, we reformulate and generalize the kernel calibration error, its estimators, and hypothesis tests using scalar-valued kernels, and evaluate the calibration of real-valued regression
problems. | https://openreview.net/pdf/2bd555e04e5c7af5c723e69ed706339c99f212a1.pdf |
Meta Back-Translation | https://openreview.net/forum?id=3jjmdp7Hha | https://openreview.net/forum?id=3jjmdp7Hha | Hieu Pham,Xinyi Wang,Yiming Yang,Graham Neubig | ICLR 2021,Poster | Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality in the pseudo-parallel data does not necessarily lead to a better final translation model, while lower-quality but diverse data often yields stronger results instead.
In this paper we propose a new way to generate pseudo-parallel data for back-translation that directly optimizes the final model performance. Specifically, we propose a meta-learning framework where the back-translation model learns to match the forward-translation model's gradients on the development data with those on the pseudo-parallel data. In our evaluations in both the standard datasets WMT En-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our method leads to significant improvements over strong baselines. | https://openreview.net/pdf/30daa849a22aebea5ef566f6b6a0f9cf99027a34.pdf |
Attentional Constellation Nets for Few-Shot Learning | https://openreview.net/forum?id=vujTf_I8Kmc | https://openreview.net/forum?id=vujTf_I8Kmc | Weijian Xu,yifan xu,Huaijin Wang,Zhuowen Tu | ICLR 2021,Poster | The success of deep convolutional neural networks builds on top of the learning of effective convolution operations, capturing a hierarchy of structured features via filtering, activation, and pooling. However, the explicit structured features, e.g. object parts, are not expressive in the existing CNN frameworks. In this paper, we tackle the few-shot learning problem and make an effort to enhance structured features by expanding CNNs with a constellation model, which performs cell feature clustering and encoding with a dense part representation; the relationships among the cell features are further modeled by an attention mechanism. With the additional constellation branch to increase the awareness of object parts, our method is able to attain the advantages of the CNNs while making the overall internal representations more robust in the few-shot learning setting. Our approach attains a significant improvement over the existing methods in few-shot learning on the CIFAR-FS, FC100, and mini-ImageNet benchmarks. | https://openreview.net/pdf/4bfc13fc5e8eadc3b396aa15c6e583195e33ef5e.pdf |
Uncertainty Estimation in Autoregressive Structured Prediction | https://openreview.net/forum?id=jN5y-zb5Q7m | https://openreview.net/forum?id=jN5y-zb5Q7m | Andrey Malinin,Mark Gales | ICLR 2021,Poster | Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimation for structured prediction tasks within a single unified and interpretable probabilistic ensemble-based framework. We consider: uncertainty estimation for sequence data at the token-level and complete sequence-level; interpretations for, and applications of, various measures of uncertainty; and discuss both the theoretical and practical challenges associated with obtaining them. This work also provides baselines for token-level and sequence-level error detection, and sequence-level out-of-domain input detection on the WMT’14 English-French and WMT’17 English-German translation and LibriSpeech speech recognition datasets. | https://openreview.net/pdf/e79c8b8744afc4b43fd9ff97d80f237b28c3125b.pdf |
Measuring Massive Multitask Language Understanding | https://openreview.net/forum?id=d7KBjmI3GmQ | https://openreview.net/forum?id=d7KBjmI3GmQ | Dan Hendrycks,Collin Burns,Steven Basart,Andy Zou,Mantas Mazeika,Dawn Song,Jacob Steinhardt | ICLR 2021,Poster | We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings. | https://openreview.net/pdf/c7127ca5fd516f0080de5b310bec7dde0c5cb907.pdf |
No MCMC for me: Amortized sampling for fast and stable training of energy-based models | https://openreview.net/forum?id=ixpSxO9flk3 | https://openreview.net/forum?id=ixpSxO9flk3 | Will Sussman Grathwohl,Jacob Jin Kelly,Milad Hashemi,Mohammad Norouzi,Kevin Swersky,David Duvenaud | ICLR 2021,Poster | Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In this work, we present a simple method for training EBMs at scale which uses an entropy-regularized generator to amortize the MCMC sampling typically used in EBM training. We improve upon prior MCMC-based entropy regularization methods with a fast variational approximation. We demonstrate the effectiveness of our approach by using it to train tractable likelihood models. Next, we apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. This allows us to extend JEM models to semi-supervised classification on tabular data from a variety of continuous domains. | https://openreview.net/pdf/4bf06b7c43a66250e22fbdad3d0f492a3293efc6.pdf |
Aligning AI With Shared Human Values | https://openreview.net/forum?id=dNy_RKzJacY | https://openreview.net/forum?id=dNy_RKzJacY | Dan Hendrycks,Collin Burns,Steven Basart,Andrew Critch,Jerry Li,Dawn Song,Jacob Steinhardt | ICLR 2021,Poster | We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values. | https://openreview.net/pdf/d05fd010cf3982444373e7567a61c45c18c0f61f.pdf |
Class Normalization for (Continual)? Generalized Zero-Shot Learning | https://openreview.net/forum?id=7pgFL2Dkyyy | https://openreview.net/forum?id=7pgFL2Dkyyy | Ivan Skorokhodov,Mohamed Elhoseiny | ICLR 2021,Poster | Normalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime. However, in the zero-shot learning (ZSL) world, these ideas have received only marginal attention. This work studies normalization in ZSL scenario from both theoretical and practical perspectives. First, we give a theoretical explanation to two popular tricks used in zero-shot learning: normalize+scale and attributes normalization and show that they help training by preserving variance during a forward pass. Next, we demonstrate that they are insufficient to normalize a deep ZSL model and propose Class Normalization (CN): a normalization scheme, which alleviates this issue both provably and in practice. Third, we show that ZSL models typically have more irregular loss surface compared to traditional classifiers and that the proposed method partially remedies this problem. Then, we test our approach on 4 standard ZSL datasets and outperform sophisticated modern SotA with a simple MLP optimized without any bells and whistles and having ~50 times faster training speed. Finally, we generalize ZSL to a broader problem — continual ZSL, and introduce some principled metrics and rigorous baselines for this new setup. The source code is available at https://github.com/universome/class-norm. | https://openreview.net/pdf/08846e7d24687d94ea8f98091ccf05e3f001d22a.pdf |
Learning explanations that are hard to vary | https://openreview.net/forum?id=hb1sDDSLbV | https://openreview.net/forum?id=hb1sDDSLbV | Giambattista Parascandolo,Alexander Neitz,Antonio Orvieto,Luigi Gresele,Bernhard Schölkopf | ICLR 2021,Poster | In this paper, we investigate the principle that good explanations are hard to vary in the context of deep learning.
We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and `patchwork' solutions that sew together different strategies, instead of identifying invariances.
To inspect this, we first formalize a notion of consistency for minima of the loss surface, which measures to what extent a minimum appears only when examples are pooled.
We then propose and experimentally validate a simple alternative algorithm based on a logical AND, that focuses on invariances and prevents memorization in a set of real-world tasks.
Finally, using a synthetic dataset with a clear distinction between invariant and spurious mechanisms, we dissect learning signals and compare this approach to well-established regularizers. | https://openreview.net/pdf/3609f7deb3e0f3fe924d4bcc0ad5015cebf79bad.pdf |
Degree-Quant: Quantization-Aware Training for Graph Neural Networks | https://openreview.net/forum?id=NSBrFgJAHg | https://openreview.net/forum?id=NSBrFgJAHg | Shyam Anil Tailor,Javier Fernandez-Marques,Nicholas Donald Lane | ICLR 2021,Poster | Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training quantized GNNs, enabling the usage of low precision integer arithmetic during inference. For GNNs seemingly unimportant choices in quantization implementation cause dramatic changes in performance. We identify the sources of error that uniquely arise when attempting to quantize GNNs, and propose an architecturally-agnostic and stable method, Degree-Quant, to improve performance over existing quantization-aware training baselines commonly used on other architectures, such as CNNs. We validate our method on six datasets and show, unlike previous quantization attempts, that models generalize to unseen graphs. Models trained with Degree-Quant for INT8 quantization perform as well as FP32 models in most cases; for INT4 models, we obtain up to 26% gains over the baselines. Our work enables up to 4.7x speedups on CPU when using INT8 arithmetic. | https://openreview.net/pdf/af4deaf6557487a509442a82fcff188259e4183a.pdf |
Byzantine-Resilient Non-Convex Stochastic Gradient Descent | https://openreview.net/forum?id=PbEHqvFtcS | https://openreview.net/forum?id=PbEHqvFtcS | Zeyuan Allen-Zhu,Faeze Ebrahimianghazani,Jerry Li,Dan Alistarh | ICLR 2021,Poster | We study adversary-resilient stochastic distributed optimization, in which $m$ machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an $\alpha$-fraction of the machines are Byzantine, in that they may behave in arbitrary, adversarial ways. We consider a variant of this procedure in the challenging non-convex case. Our main result is a new algorithm SafeguardSGD, which can provably escape saddle points and find approximate local minima of the non-convex objective. The algorithm is based on a new concentration filtering technique, and its sample and time complexity bounds match the best known theoretical bounds in the stochastic, distributed setting when no Byzantine machines are present.
Our algorithm is very practical: it improves upon the performance of all prior methods when training deep neural networks, it is relatively lightweight, and it is the first method to withstand two recently-proposed Byzantine attacks. | https://openreview.net/pdf/028c5ec3b39d900de34fdeafb26a3ada7f3abc11.pdf |
Teaching Temporal Logics to Neural Networks | https://openreview.net/forum?id=dOcQK-f4byz | https://openreview.net/forum?id=dOcQK-f4byz | Christopher Hahn,Frederik Schmitt,Jens U. Kreber,Markus Norman Rabe,Bernd Finkbeiner | ICLR 2021,Poster | We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a trace, to a given LTL formula. The training data is generated with classical solvers, which, however, only provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions to formulas, and that Transformers can predict solutions even to formulas from benchmarks from the literature on which the classical solver timed out. Transformers also generalize to the semantics of the logics: while they often deviate from the solutions found by the classical solvers, they still predict correct solutions to most formulas. | https://openreview.net/pdf/6e420870f0819a730edea6f8532d9c53257bf865.pdf |
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification | https://openreview.net/forum?id=7dpmlkBuJFC | https://openreview.net/forum?id=7dpmlkBuJFC | Yingxue Zhou,Steven Wu,Arindam Banerjee | ICLR 2021,Poster | Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each gradient update, the error rate of DP-SGD scales with the ambient dimension $p$, the number of parameters in the model. Such dependence can be problematic for over-parameterized models where $p \gg n$, the number of training samples. Existing lower bounds on private ERM show that such dependence on $p$ is inevitable in the worst case. In this paper, we circumvent the dependence on the ambient dimension by leveraging a low-dimensional structure of gradient space in deep networks---that is, the stochastic gradients for deep nets usually stay in a low dimensional subspace in the training process. We propose Projected DP-SGD that performs noise reduction by projecting the noisy gradients to a low-dimensional subspace, which is given by the top gradient eigenspace on a small public dataset. We provide a general sample complexity analysis on the public dataset for the gradient subspace identification problem and demonstrate that under certain low-dimensional assumptions the public sample complexity only grows logarithmically in $p$. Finally, we provide a theoretical analysis and empirical evaluations to show that our method can substantially improve the accuracy of DP-SGD in the high privacy regime (corresponding to low privacy loss $\epsilon$).
| https://openreview.net/pdf/72b062d9241693b3927766cc1cea70d6cd216bd2.pdf |
Generative Time-series Modeling with Fourier Flows | https://openreview.net/forum?id=PpshD0AXfA | https://openreview.net/forum?id=PpshD0AXfA | Ahmed Alaa,Alex James Chan,Mihaela van der Schaar | ICLR 2021,Poster | Generating synthetic time-series data is crucial in various application domains, such as medical prognosis, wherein research is hamstrung by the lack of access to data due to concerns over privacy. Most of the recently proposed methods for generating synthetic time-series rely on implicit likelihood modeling using generative adversarial networks (GANs)—but such models can be difficult to train, and may jeopardize privacy by “memorizing” temporal patterns in training data. In this paper, we propose an explicit likelihood model based on a novel class of normalizing flows that view time-series data in the frequency-domain rather than the time-domain. The proposed flow, dubbed a Fourier flow, uses a discrete Fourier transform (DFT) to convert variable-length time-series with arbitrary sampling periods into fixed-length spectral representations, then applies a (data-dependent) spectral filter to the frequency-transformed time-series. We show that, by virtue of the DFT analytic properties, the Jacobian determinants and inverse mapping for the Fourier flow can be computed efficiently in linearithmic time, without imposing explicit structural constraints as in existing flows such as NICE (Dinh et al. (2014)), RealNVP (Dinh et al. (2016)) and GLOW (Kingma & Dhariwal (2018)). Experiments show that Fourier flows perform competitively compared to state-of-the-art baselines. | https://openreview.net/pdf/7aad31a541edaadc936aa88af4d48ddc836b7344.pdf |
Overparameterisation and worst-case generalisation: friend or foe? | https://openreview.net/forum?id=jphnJNOwe36 | https://openreview.net/forum?id=jphnJNOwe36 | Aditya Krishna Menon,Ankit Singh Rawat,Sanjiv Kumar | ICLR 2021,Poster | Overparameterised neural networks have demonstrated the remarkable ability to perfectly fit training samples, while still generalising to unseen test samples. However, several recent works have revealed that such models' good average performance does not always translate to good worst-case performance: in particular, they may perform poorly on subgroups that are under-represented in the training set. In this paper, we show that in certain settings, overparameterised models' performance on under-represented subgroups may be improved via post-hoc processing. Specifically, such models' bias can be restricted to their classification layers, and manifest as structured prediction shifts for rare subgroups. We detail two post-hoc correction techniques to mitigate this bias, which operate purely on the outputs of standard model training. We empirically verify that with such post-hoc correction, overparameterisation can improve average and worst-case performance. | https://openreview.net/pdf/87a7cc95e2626c1015dbcadc41a7564ced3fa495.pdf |
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning | https://openreview.net/forum?id=TgSVWXw22FQ | https://openreview.net/forum?id=TgSVWXw22FQ | Siyang Yuan,Pengyu Cheng,Ruiyi Zhang,Weituo Hao,Zhe Gan,Lawrence Carin | ICLR 2021,Poster | Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made progress on voice conversion with parallel training data and pre-known speakers. However, zero-shot voice style transfer, which learns from non-parallel data and generates voices for previously unseen speakers, remains a challenging problem. In this paper we propose a novel zero-shot voice transfer method via disentangled representation learning. The proposed method first encodes speaker-related style and voice content of each input voice into separate low-dimensional embedding spaces, and then transfers to a new voice by combining the source content embedding and target style embedding through a decoder. With information-theoretic guidance, the style and content embedding spaces are representative and (ideally) independent of each other. On real-world datasets, our method outperforms other baselines and obtains state-of-the-art results in terms of transfer accuracy and voice naturalness. | https://openreview.net/pdf/eeeea139c946265a4c136e25c661eb4a6dd24f3d.pdf |
Meta-learning with negative learning rates | https://openreview.net/forum?id=60j5LygnmD | https://openreview.net/forum?id=60j5LygnmD | Alberto Bernacchia | ICLR 2021,Poster | Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target. A successful approach is meta-learning, or "learning to learn" a distribution of tasks, where "learning" is represented by an outer loop, and "to learn" by an inner loop of gradient descent. However, a number of recent empirical studies argue that the inner loop is unnecessary and more simple models work equally well or even better. We study the performance of MAML as a function of the learning rate of the inner loop, where zero learning rate implies that there is no inner loop. Using random matrix theory and exact solutions of linear models, we calculate an algebraic expression for the test loss of MAML applied to mixed linear regression and nonlinear regression with overparameterized models. Surprisingly, while the optimal learning rate for adaptation is positive, we find that the optimal learning rate for training is always negative, a setting that has never been considered before. Therefore, not only does the performance increase by decreasing the learning rate to zero, as suggested by recent work, but it can be increased even further by decreasing the learning rate to negative
values. These results help clarify under what circumstances meta-learning performs best. | https://openreview.net/pdf/af54b731e47f23c1fd6d58dd050db1ab79d41b33.pdf |
Learning to Recombine and Resample Data For Compositional Generalization | https://openreview.net/forum?id=PS3IMnScugk | https://openreview.net/forum?id=PS3IMnScugk | Ekin Akyürek,Afra Feyza Akyürek,Jacob Andreas | ICLR 2021,Poster | Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data—particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. We describe R&R, a learned data augmentation scheme that enables a large category of compositional generalizations without appeal to latent symbolic structure. R&R has two components: recombination of original training examples via a prototype-based generative model and resampling of generated examples to encourage extrapolation. Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems—instruction following (SCAN) and morphological analysis (SIGMORPHON 2018)—where R&R enables learning of new constructions and tenses from as few as eight initial examples. | https://openreview.net/pdf/4496adf916371224de150134eb36aafb36758fc7.pdf |
Fooling a Complete Neural Network Verifier | https://openreview.net/forum?id=4IwieFS44l | https://openreview.net/forum?id=4IwieFS44l | Dániel Zombori,Balázs Bánhelyi,Tibor Csendes,István Megyeri,Márk Jelasity | ICLR 2021,Poster | The efficient and accurate characterization of the robustness of neural networks to input perturbation is an important open problem. Many approaches exist including heuristic and exact (or complete) methods. Complete methods are expensive but their mathematical formulation guarantees that they provide exact robustness metrics. However, this guarantee is valid only if we assume that the verified network applies arbitrary-precision arithmetic and the verifier is reliable. In practice, however, both the networks and the verifiers apply limited-precision floating point arithmetic. In this paper, we show that numerical roundoff errors can be exploited to craft adversarial networks, in which the actual robustness and the robustness computed by a state-of-the-art complete verifier radically differ. We also show that such adversarial networks can be used to insert a backdoor into any network in such a way that the backdoor is completely missed by the verifier. The attack is easy to detect in its naive form but, as we show, the adversarial network can be transformed to make its detection less trivial. We offer a simple defense against our particular attack based on adding a very small perturbation to the network weights. However, our conjecture is that other numerical attacks are possible, and exact verification has to take into account all the details of the computation executed by the verified networks, which makes the problem significantly harder.
| https://openreview.net/pdf/77a36c9a49f48b9ddb12530a2f5dd127064dfae1.pdf |
UMEC: Unified model and embedding compression for efficient recommendation systems | https://openreview.net/forum?id=BM---bH_RSh | https://openreview.net/forum?id=BM---bH_RSh | Jiayi Shen,Haotao Wang,Shupeng Gui,Jianchao Tan,Zhangyang Wang,Ji Liu | ICLR 2021,Poster | The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios. The core part of the system is the Ranking neural network, which is usually a bottleneck of whole system performance during online inference. In this work, we propose a unified model and embedding compression (UMEC) framework to hammer an efficient neural network-based recommendation system. Our framework jointly learns input feature selection and neural network compression together, and solve them as an end-to-end resource-constrained optimization problem using ADMM. Our method outperforms other baselines in terms of neural network Flops, sparse embedding feature size and the number of sparse embedding features. We evaluate our method on the public benchmark of DLRM, trained over the Kaggle Criteo dataset. The codes can be found at https://github.com/VITA-Group/UMEC. | https://openreview.net/pdf/4b861c09a3f34663e9ee26c8e3b407a0d5f009f4.pdf |
Evaluations and Methods for Explanation through Robustness Analysis | https://openreview.net/forum?id=4dXmpCDGNp7 | https://openreview.net/forum?id=4dXmpCDGNp7 | Cheng-Yu Hsieh,Chih-Kuan Yeh,Xuanqing Liu,Pradeep Kumar Ravikumar,Seungyeon Kim,Sanjiv Kumar,Cho-Jui Hsieh | ICLR 2021,Poster | Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis. In contrast to existing evaluations which require us to specify some way to "remove" features that could inevitably introduces biases and artifacts, we make use of the subtler notion of smaller adversarial perturbations. By optimizing towards our proposed evaluation criteria, we obtain new explanations that are loosely necessary and sufficient for a prediction. We further extend the explanation to extract the set of features that would move the current prediction to a target class by adopting targeted adversarial attack for the robustness analysis. Through experiments across multiple domains and a user study, we validate the usefulness of our evaluation criteria and our derived explanations. | https://openreview.net/pdf/d6f38258eb28ef886c3aee4efb08046abbe76e5a.pdf |
Learning and Evaluating Representations for Deep One-Class Classification | https://openreview.net/forum?id=HCSgyPUfeDj | https://openreview.net/forum?id=HCSgyPUfeDj | Kihyuk Sohn,Chun-Liang Li,Jinsung Yoon,Minho Jin,Tomas Pfister | ICLR 2021,Poster | We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are faithful to the target task. We argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as a normality score from a surrogate classifier. We thoroughly evaluate different self-supervised representation learning algorithms under the proposed framework for one-class classification. Moreover, we present a novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks, including novelty and anomaly detection. Finally, we present visual explanations, confirming that the decision-making process of deep one-class classifiers is intuitive to humans. The code is available at https://github.com/google-research/deep_representation_one_class.
| https://openreview.net/pdf/8d26ea264a20bf96973da687dd1e99b4054b6388.pdf |
Conditional Negative Sampling for Contrastive Learning of Visual Representations | https://openreview.net/forum?id=v8b3e5jN66j | https://openreview.net/forum?id=v8b3e5jN66j | Mike Wu,Milan Mosse,Chengxu Zhuang,Daniel Yamins,Noah Goodman | ICLR 2021,Poster | Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two transformations of an image. NCE typically uses randomly sampled negative examples to normalize the objective, but this may often include many uninformative examples either because they are too easy or too hard to discriminate. Taking inspiration from metric learning, we show that choosing semi-hard negatives can yield stronger contrastive representations. To do this, we introduce a family of mutual information estimators that sample negatives conditionally -- in a "ring" around each positive. We prove that these estimators remain lower-bounds of mutual information, with higher bias but lower variance than NCE. Experimentally, we find our approach, applied on top of existing models (IR, CMC, and MoCo) improves accuracy by 2-5% absolute points in each case, measured by linear evaluation on four standard image benchmarks. Moreover, we find continued benefits when transferring features to a variety of new image distributions from the Meta-Dataset collection and to a variety of downstream tasks such as object detection, instance segmentation, and key-point detection. | https://openreview.net/pdf/4198b5cda9f9be5f974d70d54ad518d2c6893b99.pdf |
Communication in Multi-Agent Reinforcement Learning: Intention Sharing | https://openreview.net/forum?id=qpsl2dR9twy | https://openreview.net/forum?id=qpsl2dR9twy | Woojun Kim,Jongeui Park,Youngchul Sung | ICLR 2021,Poster | Communication is one of the core components for learning coordinated behavior in multi-agent systems.
In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generates an imagined trajectory by modeling the environment dynamics and other agents' actions. The imagined trajectory is the simulated future trajectory of each agent based on the learned model of the environment dynamics and other agents and represents each agent's future action plan. Each agent compresses this imagined trajectory capturing its future action plan to generate its intention message for communication by applying an attention mechanism to learn the relative importance of the components in the imagined trajectory based on the received message from other agents. Numeral results show that the proposed IS scheme outperforms other communication schemes in multi-agent reinforcement learning. | https://openreview.net/pdf/8ba121ac29f04d881d760a1f3b9fd0349bb591a2.pdf |
Neural Thompson Sampling | https://openreview.net/forum?id=tkAtoZkcUnm | https://openreview.net/forum?id=tkAtoZkcUnm | Weitong ZHANG,Dongruo Zhou,Lihong Li,Quanquan Gu | ICLR 2021,Poster | Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson Sampling, which adapts deep neural networks for both exploration and exploitation. At the core of our algorithm is a novel posterior distribution of the reward, where its mean is the neural network approximator, and its variance is built upon the neural tangent features of the corresponding neural network. We prove that, provided the underlying reward function is bounded, the proposed algorithm is guaranteed to achieve a cumulative regret of $O(T^{1/2})$, which matches the regret of other contextual bandit algorithms in terms of total round number $T$. Experimental comparisons with other benchmark bandit algorithms on various data sets corroborate our theory. | https://openreview.net/pdf/d0c2efae754e3efbba6032b8b7d232a28b2bf5bc.pdf |
Optimal Regularization can Mitigate Double Descent | https://openreview.net/forum?id=7R7fAoUygoa | https://openreview.net/forum?id=7R7fAoUygoa | Preetum Nakkiran,Prayaag Venkat,Sham M. Kakade,Tengyu Ma | ICLR 2021,Poster | Recent empirical and theoretical studies have shown that many learning algorithms -- from linear regression to neural networks -- can have test performance that is non-monotonic in quantities such the sample size and model size. This striking phenomenon, often referred to as "double descent", has raised questions of if we need to re-think our current understanding of generalization. In this work, we study whether the double-descent phenomenon can be avoided by using optimal regularization. Theoretically, we prove that for certain linear regression models with isotropic data distribution, optimally-tuned $\ell_2$ regularization achieves monotonic test performance as we grow either the sample size or the model size.
We also demonstrate empirically that optimally-tuned $\ell_2$ regularization can mitigate double descent for more general models, including neural networks.
Our results suggest that it may also be informative to study the test risk scalings of various algorithms in the context of appropriately tuned regularization. | https://openreview.net/pdf/3424b7750a87532de0707e6ced4dd6f62ee9ca29.pdf |
Separation and Concentration in Deep Networks | https://openreview.net/forum?id=8HhkbjrWLdE | https://openreview.net/forum?id=8HhkbjrWLdE | John Zarka,Florentin Guth,Stéphane Mallat | ICLR 2021,Poster | Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training set, and increasing the Fisher discriminant ratio. We explain this mechanism with two types of operators. We prove that a rectifier without biases applied to sign-invariant tight frames can separate class means and increase Fisher ratios. On the opposite, a soft-thresholding on tight frames can reduce within-class variabilities while preserving class means. Variance reduction bounds are proved for Gaussian mixture models. For image classification, we show that separation of class means can be achieved with rectified wavelet tight frames that are not learned. It defines a scattering transform. Learning $1 \times 1$ convolutional tight frames along scattering channels and applying a soft-thresholding reduces within-class variabilities. The resulting scattering network reaches the classification accuracy of ResNet-18 on CIFAR-10 and ImageNet, with fewer layers and no learned biases. | https://openreview.net/pdf/89800c3664ef3d1e88e5560caa77d60409b77113.pdf |
Group Equivariant Generative Adversarial Networks | https://openreview.net/forum?id=rgFNuJHHXv | https://openreview.net/forum?id=rgFNuJHHXv | Neel Dey,Antong Chen,Soheil Ghafurian | ICLR 2021,Poster | Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augmentations via additional regularizers in the GAN objective and thus spend valuable network capacity towards approximating transformation equivariance instead of their desired task. In this work, we explicitly incorporate inductive symmetry priors into the network architectures via group-equivariant convolutional networks. Group-convolutions have higher expressive power with fewer samples and lead to better gradient feedback between generator and discriminator. We show that group-equivariance integrates seamlessly with recent techniques for GAN training across regularizers, architectures, and loss functions. We demonstrate the utility of our methods for conditional synthesis by improving generation in the limited data regime across symmetric imaging datasets and even find benefits for natural images with preferred orientation. | https://openreview.net/pdf/ae61319f937a29da1acc6a953495561a0f416877.pdf |
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective | https://openreview.net/forum?id=hpH98mK5Puk | https://openreview.net/forum?id=hpH98mK5Puk | Boxin Wang,Shuohang Wang,Yu Cheng,Zhe Gan,Ruoxi Jia,Bo Li,Jingjing Liu | ICLR 2021,Poster | Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We aim to address this problem from an information-theoretic perspective, and propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models. InfoBERT contains two mutual-information-based regularizers for model training: (i) an Information Bottleneck regularizer, which suppresses noisy mutual information between the input and the feature representation; and (ii) a Robust Feature regularizer, which increases the mutual information between local robust features and global features. We provide a principled way to theoretically analyze and improve the robustness of representation learning for language models in both standard and adversarial training. Extensive experiments demonstrate that InfoBERT achieves state-of-the-art robust accuracy over several adversarial datasets on Natural Language Inference (NLI) and Question Answering (QA) tasks.
Our code is available at https://github.com/AI-secure/InfoBERT. | https://openreview.net/pdf/979f4c1546f37b773853220cc456e35122193f84.pdf |
Using latent space regression to analyze and leverage compositionality in GANs | https://openreview.net/forum?id=sjuuTm4vj0 | https://openreview.net/forum?id=sjuuTm4vj0 | Lucy Chai,Jonas Wulff,Phillip Isola | ICLR 2021,Poster | In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs. We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images from a collage of random image parts at inference time while maintaining global consistency. To compare compositional properties across different generators, we measure the trade-offs between reconstruction of the unrealistic input and image quality of the regenerated samples. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space, and we conduct experiments to quantify this independence effect. Our method is agnostic to the semantics of edits, and does not require labels or predefined concepts during training. Beyond image composition, our method extends to a number of related applications, such as image inpainting or example-based image editing, which we demonstrate on several GANs and datasets, and
because it uses only a single forward pass, it can operate in real-time. Code is available on our project page: https://chail.github.io/latent-composition/. | https://openreview.net/pdf/9d4e6357960a5481c3a1771887727b0277c2de37.pdf |
Go with the flow: Adaptive control for Neural ODEs | https://openreview.net/forum?id=giit4HdDNa | https://openreview.net/forum?id=giit4HdDNa | Mathieu Chalvidal,Matthew Ricci,Rufin VanRullen,Thomas Serre | ICLR 2021,Poster | Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations. In particular, the single flow learned by NODEs cannot express all homeomorphisms from a given data space to itself, and their static weight parameterization restricts the type of functions they can learn compared to discrete architectures with layer-dependent weights. Here, we describe a new module called neurally-controlled ODE (N-CODE) designed to improve the expressivity of NODEs. The parameters of N-CODE modules are dynamic variables governed by a trainable map from initial or current activation state, resulting in forms of open-loop and closed-loop control, respectively. A single module is sufficient for learning a distribution on non-autonomous flows that adaptively drive neural representations. We provide theoretical and empirical evidence that N-CODE circumvents limitations of previous NODEs models and show how increased model expressivity manifests in several supervised and unsupervised learning problems. These favorable empirical results indicate the potential of using data- and activity-dependent plasticity in neural networks across numerous domains. | https://openreview.net/pdf/8dc540aca5360588117ac82153292b6a2d2f4897.pdf |
A Learning Theoretic Perspective on Local Explainability | https://openreview.net/forum?id=7aL-OtQrBWD | https://openreview.net/forum?id=7aL-OtQrBWD | Jeffrey Li,Vaishnavh Nagarajan,Gregory Plumb,Ameet Talwalkar | ICLR 2021,Poster | In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditional problem of performance generalization and bound the test-time predictive accuracy of a model using a notion of how locally explainable it is. Second, we explore the novel problem of explanation generalization which is an important concern for a growing class of finite sample-based local approximation explanations. Finally, we validate our theoretical results empirically and show that they reflect what can be seen in practice. | https://openreview.net/pdf/7bc360590e883f39ece56cc91233317a9d075582.pdf |
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks | https://openreview.net/forum?id=XI-OJ5yyse | https://openreview.net/forum?id=XI-OJ5yyse | Jiaqi Ma,Bo Chang,Xuefei Zhang,Qiaozhu Mei | ICLR 2021,Poster | Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information. Conceptually, the representational information provides guidance for the model to construct better node features; while the correlational information indicates the correlation between node outcomes conditional on node features. Through a simulation study, we find that many popular GNN models are incapable of effectively utilizing the correlational information. By leveraging the idea of the copula, a principled way to describe the dependence among multivariate random variables, we offer a general solution. The proposed Copula Graph Neural Network (CopulaGNN) can take a wide range of GNN models as base models and utilize both representational and correlational information stored in the graphs. Experimental results on two types of regression tasks verify the effectiveness of the proposed method. | https://openreview.net/pdf/598a3ed080b201ac3a97abb2d3a6340a3b4bebb6.pdf |
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability | https://openreview.net/forum?id=jh-rTtvkGeM | https://openreview.net/forum?id=jh-rTtvkGeM | Jeremy Cohen,Simran Kaur,Yuanzhi Li,J Zico Kolter,Ameet Talwalkar | ICLR 2021,Poster | We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the value $2 / \text{(step size)}$, and the training loss behaves non-monotonically over short timescales, yet consistently decreases over long timescales. Since this behavior is inconsistent with several widespread presumptions in the field of optimization, our findings raise questions as to whether these presumptions are relevant to neural network training. We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability. | https://openreview.net/pdf/49962b19307f5018fe57b60a80c6cecb9a29eb02.pdf |
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly | https://openreview.net/forum?id=SlrqM9_lyju | https://openreview.net/forum?id=SlrqM9_lyju | Yuchen Jin,Tianyi Zhou,Liangyu Zhao,Yibo Zhu,Chuanxiong Guo,Marco Canini,Arvind Krishnamurthy | ICLR 2021,Poster | The learning rate (LR) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs. However, it is also one of the least automated parts of machine learning systems and usually costs significant manual effort and computing. Though there are pre-defined LR schedules and optimizers with adaptive LR, they introduce new hyperparameters that need to be tuned separately for different tasks/datasets. In this paper, we consider the question: Can we automatically tune the LR over the course of training without human involvement? We propose an efficient method, AutoLRS, which automatically optimizes the LR for each training stage by modeling training dynamics. AutoLRS aims to find an LR that minimizes the validation loss, every $\tau$ steps. We formulate it as black-box optimization and solve it by Bayesian optimization (BO). However, collecting training instances for BO requires a system to evaluate each LR queried by BO's acquisition function for $\tau$ steps, which is prohibitively expensive in practice. Instead, we apply each candidate LR for only $\tau'\ll\tau$ steps and train an exponential model to predict the validation loss after $\tau$ steps. This mutual-training process between BO and the exponential model allows us to bound the number of training steps invested in the BO search. We demonstrate the advantages and the generality of AutoLRS through extensive experiments of training DNNs from diverse domains and using different optimizers. The LR schedules auto-generated by AutoLRS leads to a speedup of $1.22\times$, $1.43\times$, and $1.5\times$ when training ResNet-50, Transformer, and BERT, respectively, compared to the LR schedules in their original papers, and an average speedup of $1.31\times$ over state-of-the-art highly tuned LR schedules. | https://openreview.net/pdf/2bf7cedff713d5a595539d7b724e1ab3e9d40b76.pdf |
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization | https://openreview.net/forum?id=kmqjgSNXby | https://openreview.net/forum?id=kmqjgSNXby | Michael R Zhang,Thomas Paine,Ofir Nachum,Cosmin Paduraru,George Tucker,ziyu wang,Mohammad Norouzi | ICLR 2021,Poster | Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics. In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We demonstrate that autoregressive dynamics models indeed outperform standard feedforward models in log-likelihood on heldout transitions. Furthermore, we compare different model-based and model-free off-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo datasets, and find that autoregressive dynamics models consistently outperform all baselines, achieving a new state-of-the-art. Finally, we show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer through data augmentation and improving performance using model-based planning.
| https://openreview.net/pdf/258fc8fbf3df2a9d9783d528b562f8f503fe1167.pdf |
On the role of planning in model-based deep reinforcement learning | https://openreview.net/forum?id=IrM64DGB21 | https://openreview.net/forum?id=IrM64DGB21 | Jessica B Hamrick,Abram L. Friesen,Feryal Behbahani,Arthur Guez,Fabio Viola,Sims Witherspoon,Thomas Anthony,Lars Holger Buesing,Petar Veličković,Theophane Weber | ICLR 2021,Poster | Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero (Schrittwieser et al., 2019), a state-of-the-art MBRL algorithm with strong connections and overlapping components with many other MBRL algorithms. We perform a number of interventions and ablations of MuZero across a wide range of environments, including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) Planning is most useful in the learning process, both for policy updates and for providing a more useful data distribution. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research. | https://openreview.net/pdf/e451ad8cff01119850da4fdb852266ebaa8184ce.pdf |
Zero-Cost Proxies for Lightweight NAS | https://openreview.net/forum?id=0cmMMy8J5q | https://openreview.net/forum?id=0cmMMy8J5q | Mohamed S Abdelfattah,Abhinav Mehrotra,Łukasz Dudziak,Nicholas Donald Lane | ICLR 2021,Poster | Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluating each model instead of full training. In this paper, we evaluate conventional reduced-training proxies and quantify how well they preserve ranking between neural network models during search when compared with the rankings produced by final trained accuracy. We propose a series of zero-cost proxies, based on recent pruning literature, that use just a single minibatch of training data to compute a model's score. Our zero-cost proxies use 3 orders of magnitude less computation but can match and even outperform conventional proxies. For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0.82, compared to 0.61 for EcoNAS (a recently proposed reduced-training proxy). Finally, we use these zero-cost proxies to enhance existing NAS search algorithms such as random search, reinforcement learning, evolutionary search and predictor-based search. For all search methodologies and across three different NAS datasets, we are able to significantly improve sample efficiency, and thereby decrease computation, by using our zero-cost proxies. For example on NAS-Bench-101, we achieved the same accuracy 4$\times$ quicker than the best previous result. Our code is made public at: https://github.com/mohsaied/zero-cost-nas. | https://openreview.net/pdf/94fe9fb70902774ff60846c845667ac64c3c8fa1.pdf |
Personalized Federated Learning with First Order Model Optimization | https://openreview.net/forum?id=ehJqJQk9cw | https://openreview.net/forum?id=ehJqJQk9cw | Michael Zhang,Karan Sapra,Sanja Fidler,Serena Yeung,Jose M. Alvarez | ICLR 2021,Poster | While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Here we propose an alternative, where each client only federates with other relevant clients to obtain a stronger model per client-specific objectives. To achieve this personalization, rather than computing a single model average with constant weights for the entire federation as in traditional FL, we efficiently calculate optimal weighted model combinations for each client, based on figuring out how much a client can benefit from another's model. We do not assume knowledge of any underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest, enabling greater flexibility for personalization. We evaluate and characterize our method on a variety of federated settings, datasets, and degrees of local data heterogeneity. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions. | https://openreview.net/pdf/f4b5b0c02b8e08918168f163c5f057cd709802b9.pdf |
Deconstructing the Regularization of BatchNorm | https://openreview.net/forum?id=d-XzF81Wg1 | https://openreview.net/forum?id=d-XzF81Wg1 | Yann Dauphin,Ekin Dogus Cubuk | ICLR 2021,Poster | Batch normalization (BatchNorm) has become a standard technique in deep learning. Its popularity is in no small part due to its often positive effect on generalization. Despite this success, the regularization effect of the technique is still poorly understood. This study aims to decompose BatchNorm into separate mechanisms that are much simpler. We identify three effects of BatchNorm and assess their impact directly with ablations and interventions. Our experiments show that preventing explosive growth at the final layer at initialization and during training can recover a large part of BatchNorm's generalization boost. This regularization mechanism can lift accuracy by $2.9\%$ for Resnet-50 on Imagenet without BatchNorm. We show it is linked to other methods like Dropout and recent initializations like Fixup. Surprisingly, this simple mechanism matches the improvement of $0.9\%$ of the more complex Dropout regularization for the state-of-the-art Efficientnet-B8 model on Imagenet. This demonstrates the underrated effectiveness of simple regularizations and sheds light on directions to further improve generalization for deep nets. | https://openreview.net/pdf/a940f4ffe517172e86f0802bdffb5c6f0a602068.pdf |
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks | https://openreview.net/forum?id=193sEnKY1ij | https://openreview.net/forum?id=193sEnKY1ij | Shyamgopal Karthik,Ameya Prabhu,Puneet K. Dokania,Vineet Gandhi | ICLR 2021,Poster | There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not always show practical improvement over the standard cross-entropy baseline in making better mistakes. The reason for the reduction in average mistake-severity can be attributed to the increase in low-severity mistakes, which may also explain the noticeable drop in their accuracy. To this end, we use the classical Conditional Risk Minimization (CRM) framework for hierarchy-aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra hyperparameters and requires adding just a few lines of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-$k$ predictions across datasets, with very little loss in accuracy. CRM, because of its simplicity, can be used with any off-the-shelf trained model that provides reliable likelihood estimates. | https://openreview.net/pdf/1a3cb262b1a7bf8a9f26a4734b5d77ea7f544937.pdf |
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures | https://openreview.net/forum?id=l0mSUROpwY | https://openreview.net/forum?id=l0mSUROpwY | Pedro Hermosilla,Marco Schäfer,Matej Lang,Gloria Fackelmann,Pere-Pau Vázquez,Barbora Kozlikova,Michael Krone,Tobias Ritschel,Timo Ropinski | ICLR 2021,Poster | Proteins perform a large variety of functions in living organisms and thus play a key role in biology. However, commonly used algorithms in protein representation learning were not specifically designed for protein data, and are therefore not able to capture all relevant structural levels of a protein during learning. To fill this gap, we propose two new learning operators, specifically designed to process protein structures. First, we introduce a novel convolution operator that considers the primary, secondary, and tertiary structure of a protein by using $n$-D convolutions defined on both the Euclidean distance, as well as multiple geodesic distances between the atoms in a multi-graph. Second, we introduce a set of hierarchical pooling operators that enable multi-scale protein analysis. We further evaluate the accuracy of our algorithms on common downstream tasks, where we outperform state-of-the-art protein learning algorithms. | https://openreview.net/pdf/06a67dcf3e870e158211013994abdc6c41706fea.pdf |
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule | https://openreview.net/forum?id=45uOPa46Kh | https://openreview.net/forum?id=45uOPa46Kh | Shuhei Kurita,Kyunghyun Cho | ICLR 2021,Poster | Vision-and-language navigation (VLN) is a task in which an agent is embodied in a realistic 3D environment and follows an instruction to reach the goal node. While most of the previous studies have built and investigated a discriminative approach, we notice that there are in fact two possible approaches to building such a VLN agent: discriminative and generative. In this paper, we design and investigate a generative language-grounded policy which uses a language model to compute the distribution over all possible instructions i.e. all possible sequences of vocabulary tokens given action and the transition history. In experiments, we show that the proposed generative approach outperforms the discriminative approach in the Room-2-Room (R2R) and Room-4-Room (R4R) datasets, especially in the unseen environments. We further show that the combination of the generative and discriminative policies achieves close to the state-of-the art results in the R2R dataset, demonstrating that the generative and discriminative policies capture the different aspects of VLN. | https://openreview.net/pdf/9971055694256fe2362f6a3739e1101c82e2941a.pdf |
Learning a Latent Search Space for Routing Problems using Variational Autoencoders | https://openreview.net/forum?id=90JprVrJBO | https://openreview.net/forum?id=90JprVrJBO | André Hottung,Bhanu Bhandari,Kevin Tierney | ICLR 2021,Poster | Methods for automatically learning to solve routing problems are rapidly improving in performance. While most of these methods excel at generating solutions quickly, they are unable to effectively utilize longer run times because they lack a sophisticated search component. We present a learning-based optimization approach that allows a guided search in the distribution of high-quality solutions for a problem instance. More precisely, our method uses a conditional variational autoencoder that learns to map points in a continuous (latent) search space to high-quality, instance-specific routing problem solutions. The learned space can then be searched by any unconstrained continuous optimization method. We show that even using a standard differential evolution search strategy our approach is able to outperform existing purely machine learning based approaches. | https://openreview.net/pdf/4be79a469cf7db5216d43cb8af17c5e397e9033b.pdf |
Mastering Atari with Discrete World Models | https://openreview.net/forum?id=0oabwyZbOu | https://openreview.net/forum?id=0oabwyZbOu | Danijar Hafner,Timothy P Lillicrap,Mohammad Norouzi,Jimmy Ba | ICLR 2021,Poster | Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, modeling Atari games accurately enough to derive successful behaviors has remained an open challenge for many years. We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model. The world model uses discrete representations and is trained separately from the policy. DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model. With the same computational budget and wall-clock time, Dreamer V2 reaches 200M frames and surpasses the final performance of the top single-GPU agents IQN and Rainbow. DreamerV2 is also applicable to tasks with continuous actions, where it learns an accurate world model of a complex humanoid robot and solves stand-up and walking from only pixel inputs. | https://openreview.net/pdf/1a697645438f4372744f991c8a726b8b9a004598.pdf |
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling | https://openreview.net/forum?id=I4c4K9vBNny | https://openreview.net/forum?id=I4c4K9vBNny | Đorđe Miladinović,Aleksandar Stanić,Stefan Bauer,Jürgen Schmidhuber,Joachim M. Buhmann | ICLR 2021,Poster | How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class. Furthermore, we demonstrate that SDN can be applied to large images by synthesizing samples of high quality and coherence. In a vanilla VAE setting, we find that a powerful SDN decoder also improves learning disentangled representations, indicating that neural architectures play an important role in this task. Our results suggest favoring spatial dependency over convolutional layers in various VAE settings. The accompanying source code is given at https://github.com/djordjemila/sdn. | https://openreview.net/pdf/07c9cfa0f97d0d062ba56a4bd3f00cbb1488c8a9.pdf |
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation | https://openreview.net/forum?id=uR9LaO_QxF | https://openreview.net/forum?id=uR9LaO_QxF | Emilio Parisotto,Russ Salakhutdinov | ICLR 2021,Poster | Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on un-accelerated hardware such as CPUs, which likewise restricts model size to prevent intractable experiment run times. These "actor-latency" constrained settings present a major obstruction to the scaling up of model complexity that has recently been extremely successful in supervised learning. To be able to utilize large model capacity while still operating within the limits imposed by the system during acting, we develop an "Actor-Learner Distillation" (ALD) procedure that leverages a continual form of distillation that transfers learning progress from a large capacity learner model to a small capacity actor model. As a case study, we develop this procedure in the context of partially-observable environments, where transformer models have had large improvements over LSTMs recently, at the cost of significantly higher computational complexity. With transformer models as the learner and LSTMs as the actor, we demonstrate in several challenging memory environments that using Actor-Learner Distillation largely recovers the clear sample-efficiency gains of the transformer learner model while maintaining the fast inference and reduced total training time of the LSTM actor model. | https://openreview.net/pdf/2384835e520b07abfe36d0826a5cd6dc0673f653.pdf |
IOT: Instance-wise Layer Reordering for Transformer Structures | https://openreview.net/forum?id=ipUPfYxWZvM | https://openreview.net/forum?id=ipUPfYxWZvM | Jinhua Zhu,Lijun Wu,Yingce Xia,Shufang Xie,Tao Qin,Wengang Zhou,Houqiang Li,Tie-Yan Liu | ICLR 2021,Poster | With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the \emph{layer order} is fixed and kept the same across data samples. We observe that different data samples actually favor different orders of the layers. Based on this observation, in this work, we break the assumption of the fixed layer order in Transformer and introduce instance-wise layer reordering into model structure. Our Instance-wise Ordered Transformer (IOT) can model variant functions by reordered layers, which enables each sample to select the better one to improve the model performance under the constraint of almost same number of parameters. To achieve this, we introduce a light predictor with negligible parameter and inference cost to decide the most capable and favorable layer order for any input sequence. Experiments on $3$ tasks (neural machine translation, abstractive summarization, and code generation) and $9$ datasets demonstrate consistent improvements of our method. We further show that our method can also be applied to other architectures beyond Transformer. Our code is released at Github\footnote{\url{https://github.com/instance-wise-ordered-transformer/IOT}}. | https://openreview.net/pdf/46acb63013c8b064e958eae0cc405ba84b5cbdb5.pdf |
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | https://openreview.net/forum?id=GFsU8a0sGB | https://openreview.net/forum?id=GFsU8a0sGB | Maruan Al-Shedivat,Jennifer Gillenwater,Eric Xing,Afshin Rostamizadeh | ICLR 2021,Poster | Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learning as a posterior inference problem, where the goal is to infer a global posterior distribution by having client devices each infer the posterior of their local data. While exact inference is often intractable, this perspective provides a principled way to search for global optima in federated settings. Further, starting with the analysis of federated quadratic objectives, we develop a computation- and communication-efficient approximate posterior inference algorithm—federated posterior averaging (FedPA). Our algorithm uses MCMC for approximate inference of local posteriors on the clients and efficiently communicates their statistics to the server, where the latter uses them to refine a global estimate of the posterior mode. Finally, we show that FedPA generalizes federated averaging (FedAvg), can similarly benefit from adaptive optimizers, and yields state-of-the-art results on four realistic and challenging benchmarks, converging faster, to better optima. | https://openreview.net/pdf/3c19f2476503b117ff059dbfc938d5efd211ed0f.pdf |
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers | https://openreview.net/forum?id=Nc3TJqbcl3 | https://openreview.net/forum?id=Nc3TJqbcl3 | Cristina Pinneri,Shambhuraj Sawant,Sebastian Blaes,Georg Martius | ICLR 2021,Poster | Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning community. However, this success comes at the cost of high computational complexity, being therefore not suitable for real-time control. In this paper, we propose a technique to jointly optimize the trajectory and distill a policy, which is essential for fast execution in real robotic systems. Our method builds upon standard approaches, like guidance cost and dataset aggregation, and introduces a novel adaptive factor which prevents the optimizer from collapsing to the learner's behavior at the beginning of the training. The extracted policies reach unprecedented performance on challenging tasks as making a humanoid stand up and opening a door without reward shaping | https://openreview.net/pdf/1e36a9b55c2b184bab1395be47101e4beb882f41.pdf |
BREEDS: Benchmarks for Subpopulation Shift | https://openreview.net/forum?id=mQPBmvyAuk | https://openreview.net/forum?id=mQPBmvyAuk | Shibani Santurkar,Dimitris Tsipras,Aleksander Madry | ICLR 2021,Poster | We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. This enables us to synthesize realistic distribution shifts whose sources can be precisely controlled and characterized, within existing
large-scale datasets. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity. We then validate that the corresponding shifts are tractable by obtaining human baselines. Finally, we utilize these benchmarks to measure the sensitivity of standard model architectures as well as the effectiveness of existing train-time robustness interventions. | https://openreview.net/pdf/267e1b0387f6edaaa3b1145def1009b6803d55b6.pdf |
On the Impossibility of Global Convergence in Multi-Loss Optimization | https://openreview.net/forum?id=NQbnPjPYaG6 | https://openreview.net/forum?id=NQbnPjPYaG6 | Alistair Letcher | ICLR 2021,Poster | Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting. This is known to break down for vanilla gradient descent when moving to multi-loss optimization, but can we hope to build some algorithm with global guarantees? We negatively resolve this open problem by proving that desirable convergence properties cannot simultaneously hold for any algorithm. Our result has more to do with the existence of games with no satisfactory outcomes, than with algorithms per se. More explicitly we construct a two-player game with zero-sum interactions whose losses are both coercive and analytic, but whose only simultaneous critical point is a strict maximum. Any 'reasonable' algorithm, defined to avoid strict maxima, will therefore fail to converge. This is fundamentally different from single losses, where coercivity implies existence of a global minimum. Moreover, we prove that a wide range of existing gradient-based methods almost surely have bounded but non-convergent iterates in a constructed zero-sum game for suitably small learning rates. It nonetheless remains an open question whether such behavior can arise in high-dimensional games of interest to ML practitioners, such as GANs or multi-agent RL. | https://openreview.net/pdf/baee1df38dd94202c599b2c6090013486763252f.pdf |
Efficient Wasserstein Natural Gradients for Reinforcement Learning | https://openreview.net/forum?id=OHgnfSrn2jv | https://openreview.net/forum?id=OHgnfSrn2jv | Ted Moskovitz,Michael Arbel,Ferenc Huszar,Arthur Gretton | ICLR 2021,Poster | A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally efficient \emph{Wasserstein natural gradient} (WNG) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed optimization. This method follows the recent theme in RL of including divergence penalties in the objective to establish trust regions. Experiments on challenging tasks demonstrate improvements in both computational cost and performance over advanced baselines.
| https://openreview.net/pdf/1c8cd7b02df8016f8cf8a6a2e70844ad5ae87de9.pdf |
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks | https://openreview.net/forum?id=C70cp4Cn32 | https://openreview.net/forum?id=C70cp4Cn32 | Timothy Castiglia,Anirban Das,Stacy Patterson | ICLR 2021,Poster | We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint sub-networks, with a single hub and multiple workers; further, workers may have different operating rates. The hubs exchange information with one another via a connected, but not necessarily complete communication network. In our algorithm, sub-networks execute a distributed SGD algorithm, using a hub-and-spoke paradigm, and the hubs periodically average their models with neighboring hubs. We first provide a unified mathematical framework that describes the Multi-Level Local SGD algorithm. We then present a theoretical analysis of the algorithm; our analysis shows the dependence of the convergence error on the worker node heterogeneity, hub network topology, and the number of local, sub-network, and global iterations. We illustrate the effectiveness of our algorithm in a multi-level network with slow workers via simulation-based experiments. | https://openreview.net/pdf/8dbde4c960f1598d19e7201058a77a1224b4a939.pdf |
Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory | https://openreview.net/forum?id=QoWatN-b8T | https://openreview.net/forum?id=QoWatN-b8T | Jason Ramapuram,Yan Wu,Alexandros Kalousis | ICLR 2021,Poster | Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work, we develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory via a hierarchical latent variable model. We take inspiration from traditional heap allocation and extend the idea of locally contiguous memory to the Kanerva Machine, enabling a novel differentiable block allocated latent memory. In contrast to the Kanerva Machine, we simplify the process of memory writing by treating it as a fully feed forward deterministic process, relying on the stochasticity of the read key distribution to disperse information within the memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation, resulting in new state-of-the-art conditional likelihood values on binarized MNIST (≤41.58 nats/image) , binarized Omniglot (≤66.24 nats/image), as well as presenting competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32×32. | https://openreview.net/pdf/70c8013c1c3775393cfb1b86a14218089c000691.pdf |
EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS | https://openreview.net/forum?id=hsFN92eQEla | https://openreview.net/forum?id=hsFN92eQEla | Like Hui,Mikhail Belkin | ICLR 2021,Poster | Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded.
We explore several major neural architectures and a range of standard benchmark datasets for NLP, automatic speech recognition (ASR) and computer vision tasks to show that these architectures, with the same hyper-parameter settings as reported in the literature, perform comparably or better when trained with the square loss, even after equalizing computational resources.
Indeed, we observe that the square loss produces better results in the dominant majority of NLP and ASR experiments. Cross-entropy appears to have a slight edge on computer vision tasks.
We argue that there is little compelling empirical or theoretical evidence indicating a clear-cut advantage to the cross-entropy loss. Indeed, in our experiments, performance on nearly all non-vision tasks can be improved, sometimes significantly, by switching to the square loss. Furthermore, training with square loss appears to be less sensitive to the randomness in initialization. We posit that
training using the square loss for classification needs to be a part of best practices of modern deep learning on equal footing with cross-entropy. | https://openreview.net/pdf/374e8d19ecea989cd40fe8978d54d9435fb6a677.pdf |
Self-supervised Learning from a Multi-view Perspective | https://openreview.net/forum?id=-bdp_8Itjwp | https://openreview.net/forum?id=-bdp_8Itjwp | Yao-Hung Hubert Tsai,Yue Wu,Ruslan Salakhutdinov,Louis-Philippe Morency | ICLR 2021,Poster | As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as object detection and image captioning. Many proposed approaches for self-supervised learning follow naturally a multi-view perspective, where the input (e.g., original images) and the self-supervised signals (e.g., augmented images) can be seen as two redundant views of the data. Building from this multi-view perspective, this paper provides an information-theoretical framework to better understand the properties that encourage successful self-supervised learning. Specifically, we demonstrate that self-supervised learned representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design. In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information. To verify our analysis, we conduct controlled experiments to evaluate the impact of the composite objectives. We also explore our framework's empirical generalization beyond the multi-view perspective, where the cross-view redundancy may not be clearly observed. | https://openreview.net/pdf/07896a10dbc5362cc803196d61c5ce639ef2bceb.pdf |
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels | https://openreview.net/forum?id=j9Rv7qdXjd | https://openreview.net/forum?id=j9Rv7qdXjd | Binxin Ru,Xingchen Wan,Xiaowen Dong,Michael Osborne | ICLR 2021,Poster | Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or how we should modify the architecture if we want further improvements. We propose a Bayesian optimisation (BO) approach for NAS that combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate. Our method not only optimises the architecture in a highly data-efficient manner, but also affords interpretability by discovering useful network features and their corresponding impact on the network performance. Moreover, our method is capable of capturing the topological structures of the architectures and is scalable to large graphs, thus making the high-dimensional and graph-like search spaces amenable to BO. We demonstrate empirically that our surrogate model is capable of identifying useful motifs which can guide the generation of new architectures. We finally show that our method outperforms existing NAS approaches to achieve the state of the art on both closed- and open-domain search spaces. | https://openreview.net/pdf/7a44783a6b1ada0693c095a8030ac42d0cdcf005.pdf |
CT-Net: Channel Tensorization Network for Video Classification | https://openreview.net/forum?id=UoaQUQREMOs | https://openreview.net/forum?id=UoaQUQREMOs | Kunchang Li,Xianhang Li,Yali Wang,Jun Wang,Yu Qiao | ICLR 2021,Poster | 3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions. Unfortunately, most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficiency. For this reason, we propose a concise and novel Channel Tensorization Network (CT-Net), by treating the channel dimension of input feature as a multiplication of K sub-dimensions. On one hand, it naturally factorizes convolution in a multiple dimension way, leading to a light computation burden. On the other hand, it can effectively enhance feature interaction from different channels, and progressively enlarge the 3D receptive field of such interaction to boost classification accuracy. Furthermore, we equip our CT-Module with a Tensor Excitation (TE) mechanism. It can learn to exploit spatial, temporal and channel attention in a high-dimensional manner, to improve the cooperative power of all the feature dimensions in our CT-Module. Finally, we flexibly adapt ResNet as our CT-Net. Extensive experiments are conducted on several challenging video benchmarks, e.g., Kinetics-400, Something-Something V1 and V2. Our CT-Net outperforms a number of recent SOTA approaches, in terms of accuracy and/or efficiency. | https://openreview.net/pdf/c320f306f4e64aa4b2f77386c7128cf4b1caef92.pdf |
Intraclass clustering: an implicit learning ability that regularizes DNNs | https://openreview.net/forum?id=tqOvYpjPax2 | https://openreview.net/forum?id=tqOvYpjPax2 | Simon Carbonnelle,Christophe De Vleeschouwer | ICLR 2021,Poster | Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood. In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitutes an implicit form of regularization, as no explicit training mechanisms or supervision target such behaviour. To support our hypothesis, we design four different measures of intraclass clustering, based on the neuron- and layer-level representations of the training data. We then show that these measures constitute accurate predictors of generalization performance across variations of a large set of hyperparameters (learning rate, batch size, optimizer, weight decay, dropout rate, data augmentation, network depth and width). | https://openreview.net/pdf/ad12f1d02acbf2f8844ae158fd9d58309060ee34.pdf |
Learning Robust State Abstractions for Hidden-Parameter Block MDPs | https://openreview.net/forum?id=fmOOI2a3tQP | https://openreview.net/forum?id=fmOOI2a3tQP | Amy Zhang,Shagun Sodhani,Khimya Khetarpal,Joelle Pineau | ICLR 2021,Poster | Many control tasks exhibit similar dynamics that can be modeled as having common latent structure. Hidden-Parameter Markov Decision Processes (HiP-MDPs) explicitly model this structure to improve sample efficiency in multi-task settings.
However, this setting makes strong assumptions on the observability of the state that limit its application in real-world scenarios with rich observation spaces. In this work, we leverage ideas of common structure from the HiP-MDP setting, and extend it to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework for both multi-task reinforcement learning (MTRL) and meta-reinforcement learning (Meta-RL) settings. Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work. To further demonstrate efficacy of the proposed method, we empirically compare and show improvement over multi-task and meta-reinforcement learning baselines. | https://openreview.net/pdf/acd2e5fe7849505e5c63b1341d5225ee12a494be.pdf |
Isometric Transformation Invariant and Equivariant Graph Convolutional Networks | https://openreview.net/forum?id=FX0vR39SJ5q | https://openreview.net/forum?id=FX0vR39SJ5q | Masanobu Horie,Naoki Morita,Toshiaki Hishinuma,Yu Ihara,Naoto Mitsume | ICLR 2021,Poster | Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is learning and inferring the isometric transformation invariant and equivariant features in a computationally efficient manner. In this paper, we propose a set of transformation invariant and equivariant models based on graph convolutional networks, called IsoGCNs. We demonstrate that the proposed model has a competitive performance compared to state-of-the-art methods on tasks related to geometrical and physical simulation data. Moreover, the proposed model can scale up to graphs with 1M vertices and conduct an inference faster than a conventional finite element analysis, which the existing equivariant models cannot achieve. | https://openreview.net/pdf/dc6ec76f7b5bbad9a8a07d1ff870306354097c3c.pdf |
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates | https://openreview.net/forum?id=P6_q1BRxY8Q | https://openreview.net/forum?id=P6_q1BRxY8Q | Zengyi Qin,Kaiqing Zhang,Yuxiao Chen,Jingkai Chen,Chuchu Fan | ICLR 2021,Poster | We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals. Our core idea is to learn the multi-agent control policy jointly with learning the control barrier functions as safety certificates. We propose a new joint-learning framework that can be implemented in a decentralized fashion, which can adapt to an arbitrarily large number of agents. Building upon this framework, we further improve the scalability by incorporating neural network architectures that are invariant to the quantity and permutation of neighboring agents. In addition, we propose a new spontaneous policy refinement method to further enforce the certificate condition during testing. We provide extensive experiments to demonstrate that our method significantly outperforms other leading multi-agent control approaches in terms of maintaining safety and completing original tasks. Our approach also shows substantial generalization capability in that the control policy can be trained with 8 agents in one scenario, while being used on other scenarios with up to 1024 agents in complex multi-agent environments and dynamics. Videos and source code can be found at https://realm.mit.edu/blog/learning-safe-multi-agent-control-decentralized-neural-barrier-certificates. | https://openreview.net/pdf/f4c73ba388f2e06c4eb91e11f62979b68fb177f2.pdf |
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