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A Critique of Self-Expressive Deep Subspace Clustering | https://openreview.net/forum?id=FOyuZ26emy | https://openreview.net/forum?id=FOyuZ26emy | Benjamin David Haeffele,Chong You,Rene Vidal | ICLR 2021,Poster | Subspace clustering is an unsupervised clustering technique designed to cluster data that is supported on a union of linear subspaces, with each subspace defining a cluster with dimension lower than the ambient space. Many existing formulations for this problem are based on exploiting the self-expressive property of linear subspaces, where any point within a subspace can be represented as linear combination of other points within the subspace. To extend this approach to data supported on a union of non-linear manifolds, numerous studies have proposed learning an embedding of the original data using a neural network which is regularized by a self-expressive loss function on the data in the embedded space to encourage a union of linear subspaces prior on the data in the embedded space. Here we show that there are a number of potential flaws with this approach which have not been adequately addressed in prior work. In particular, we show the model formulation is often ill-posed in that it can lead to a degenerate embedding of the data, which need not correspond to a union of subspaces at all and is poorly suited for clustering. We validate our theoretical results experimentally and also repeat prior experiments reported in the literature, where we conclude that a significant portion of the previously claimed performance benefits can be attributed to an ad-hoc post processing step rather than the deep subspace clustering model. | https://openreview.net/pdf/b695594106c11bb76d5334e21cd6ae28728ec857.pdf |
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning | https://openreview.net/forum?id=unI5ucw_Jk | https://openreview.net/forum?id=unI5ucw_Jk | Alihan Hüyük,Daniel Jarrett,Cem Tekin,Mihaela van der Schaar | ICLR 2021,Poster | Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker’s policy is challenging—with no access to underlying states, no knowledge of environment dynamics, and no allowance for live experimentation. We desire learning a data-driven representation of decision- making behavior that (1) inheres transparency by design, (2) accommodates partial observability, and (3) operates completely offline. To satisfy these key criteria, we propose a novel model-based Bayesian method for interpretable policy learning (“Interpole”) that jointly estimates an agent’s (possibly biased) belief-update process together with their (possibly suboptimal) belief-action mapping. Through experiments on both simulated and real-world data for the problem of Alzheimer’s disease diagnosis, we illustrate the potential of our approach as an investigative device for auditing, quantifying, and understanding human decision-making behavior. | https://openreview.net/pdf/87d4ea3176fd5ca710f4f3d549c2f1c25b5898cf.pdf |
For self-supervised learning, Rationality implies generalization, provably | https://openreview.net/forum?id=Srmggo3b3X6 | https://openreview.net/forum?id=Srmggo3b3X6 | Yamini Bansal,Gal Kaplun,Boaz Barak | ICLR 2021,Poster | We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation $r$ of the training~data, and then fitting a simple (e.g., linear) classifier $g$ to the labels. Specifically, we show that (under the assumptions described below) the generalization gap of such classifiers tends to zero if $\mathsf{C}(g) \ll n$, where $\mathsf{C}(g)$ is an appropriately-defined measure of the simple classifier $g$'s complexity, and $n$ is the number of training samples. We stress that our bound is independent of the complexity of the representation $r$.
We do not make any structural or conditional-independence assumptions on the representation-learning task, which can use the same training dataset that is later used for classification. Rather, we assume that the training procedure satisfies certain natural noise-robustness (adding small amount of label noise causes small degradation in performance) and rationality (getting the wrong label is not better than getting no label at all) conditions that widely hold across many standard architectures.
We also conduct an extensive empirical study of the generalization gap and the quantities used in our assumptions for a variety of self-supervision based algorithms, including SimCLR, AMDIM and BigBiGAN, on the CIFAR-10 and ImageNet datasets. We show that, unlike standard supervised classifiers, these algorithms display small generalization gap, and the bounds we prove on this gap are often non vacuous. | https://openreview.net/pdf/111c52ee7a399d9253f0dc005866fa6a4b705dc7.pdf |
Directed Acyclic Graph Neural Networks | https://openreview.net/forum?id=JbuYF437WB6 | https://openreview.net/forum?id=JbuYF437WB6 | Veronika Thost,Jie Chen | ICLR 2021,Poster | Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most common GNN architecture aggregates information from neighborhoods based on message passing. Its generality has made it broadly applicable. In this paper, we focus on a special, yet widely used, type of graphs---DAGs---and inject a stronger inductive bias---partial ordering---into the neural network design. We propose the directed acyclic graph neural network, DAGNN, an architecture that processes information according to the flow defined by the partial order. DAGNN can be considered a framework that entails earlier works as special cases (e.g., models for trees and models updating node representations recurrently), but we identify several crucial components that prior architectures lack. We perform comprehensive experiments, including ablation studies, on representative DAG datasets (i.e., source code, neural architectures, and probabilistic graphical models) and demonstrate the superiority of DAGNN over simpler DAG architectures as well as general graph architectures. | https://openreview.net/pdf/435a89f1a5c579c1c5fe7ba3ebef81c09224e075.pdf |
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control | https://openreview.net/forum?id=N3zUDGN5lO | https://openreview.net/forum?id=N3zUDGN5lO | Vitaly Kurin,Maximilian Igl,Tim Rocktäschel,Wendelin Boehmer,Shimon Whiteson | ICLR 2021,Poster | Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address incompatible environments, because they can process graphs of arbitrary size. They also allow practitioners to inject biases encoded in the structure of the input graph. Existing work in graph-based continuous control uses the physical morphology of the agent to construct the input graph, i.e., encoding limb features as node labels and using edges to connect the nodes if their corresponded limbs are physically connected.
In this work, we present a series of ablations on existing methods that show that morphological information encoded in the graph does not improve their performance. Motivated by the hypothesis that any benefits GNNs extract from the graph structure are outweighed by difficulties they create for message passing, we also propose Amorpheus, a transformer-based approach. Further results show that, while Amorpheus ignores the morphological information that GNNs encode, it nonetheless substantially outperforms GNN-based methods. | https://openreview.net/pdf/130809fe4dd2abe36b6f0c395f8cc2f51174bc4d.pdf |
Incremental few-shot learning via vector quantization in deep embedded space | https://openreview.net/forum?id=3SV-ZePhnZM | https://openreview.net/forum?id=3SV-ZePhnZM | Kuilin Chen,Chi-Guhn Lee | ICLR 2021,Poster | The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting. This challenge becomes greater when novel tasks contain very few labelled training samples. Currently, most methods are dedicated to class-incremental learning and rely on sufficient training data to learn additional weights for newly added classes. Those methods cannot be easily extended to incremental regression tasks and could suffer from severe overfitting when learning few-shot novel tasks. In this study, we propose a nonparametric method in deep embedded space to tackle incremental few-shot learning problems. The knowledge about the learned tasks are compressed into a small number of quantized reference vectors. The proposed method learns new tasks sequentially by adding more reference vectors to the model using few-shot samples in each novel task. For classification problems, we employ the nearest neighbor scheme to make classification on sparsely available data and incorporate intra-class variation, less forgetting regularization and calibration of reference vectors to mitigate catastrophic forgetting. In addition, the proposed learning vector quantization (LVQ) in deep embedded space can be customized as a kernel smoother to handle incremental few-shot regression tasks. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in incremental learning. | https://openreview.net/pdf/e34aea7744f0d7fa06719626e37f52420d1dc247.pdf |
Revisiting Few-sample BERT Fine-tuning | https://openreview.net/forum?id=cO1IH43yUF | https://openreview.net/forum?id=cO1IH43yUF | Tianyi Zhang,Felix Wu,Arzoo Katiyar,Kilian Q Weinberger,Yoav Artzi | ICLR 2021,Poster | This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network for down-stream tasks; and the prevalent practice of using a pre-determined, and small number of training iterations. We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process. In light of these observations, we re-visit recently proposed methods to improve few-sample fine-tuning with BERT and re-evaluate their effectiveness. Generally, we observe the impact of these methods diminishes significantly with our modified process. | https://openreview.net/pdf/b9891ff9bbfe3c50cf752711eb45ea789cd534aa.pdf |
Linear Convergent Decentralized Optimization with Compression | https://openreview.net/forum?id=84gjULz1t5 | https://openreview.net/forum?id=84gjULz1t5 | Xiaorui Liu,Yao Li,Rongrong Wang,Jiliang Tang,Ming Yan | ICLR 2021,Poster | Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivated by primal-dual algorithms, this paper proposes the first \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, LEAD. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error, and we provide the first consensus error bound in such settings without assuming bounded gradients. Experiments on convex problems validate our theoretical analysis, and empirical study on deep neural nets shows that LEAD is applicable to non-convex problems. | https://openreview.net/pdf/2c18c598153405d899eb172db4db2e112b25c66f.pdf |
Learning from Demonstration with Weakly Supervised Disentanglement | https://openreview.net/forum?id=Ldau9eHU-qO | https://openreview.net/forum?id=Ldau9eHU-qO | Yordan Hristov,Subramanian Ramamoorthy | ICLR 2021,Poster | Robotic manipulation tasks, such as wiping with a soft sponge, require control from multiple rich sensory modalities. Human-robot interaction, aimed at teach- ing robots, is difficult in this setting as there is potential for mismatch between human and machine comprehension of the rich data streams. We treat the task of interpretable learning from demonstration as an optimisation problem over a probabilistic generative model. To account for the high-dimensionality of the data, a high-capacity neural network is chosen to represent the model. The latent variables in this model are explicitly aligned with high-level notions and concepts that are manifested in a set of demonstrations. We show that such alignment is best achieved through the use of labels from the end user, in an appropriately restricted vocabulary, in contrast to the conventional approach of the designer picking a prior over the latent variables. Our approach is evaluated in the context of two table-top robot manipulation tasks performed by a PR2 robot – that of dabbing liquids with a sponge (forcefully pressing a sponge and moving it along a surface) and pouring between different containers. The robot provides visual information, arm joint positions and arm joint efforts. We have made videos of the tasks and data available - see supplementary materials at: https://sites.google.com/view/weak-label-lfd. | https://openreview.net/pdf/49356704899c9011f84ed5a7f4cb44bbe43ae737.pdf |
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization | https://openreview.net/forum?id=wta_8Hx2KD | https://openreview.net/forum?id=wta_8Hx2KD | Rui Wang,Robin Walters,Rose Yu | ICLR 2021,Poster | Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to generalize under distributional shift limit its applicability to the real world. We propose to improve accuracy and generalization by incorporating symmetries into convolutional neural networks. Specifically, we employ a variety of methods each tailored to enforce a different symmetry. Our models are both theoretically and experimentally robust to distributional shift by symmetry group transformations and enjoy favorable sample complexity. We demonstrate the advantage of our approach on a variety of physical dynamics including Rayleigh–Bénard convection and real-world ocean currents and temperatures. Compare with image or text applications, our work is a significant step towards applying equivariant neural networks to high-dimensional systems with complex dynamics. | https://openreview.net/pdf/0baeb937c065c6c783ae239c7722374340b6497e.pdf |
The Risks of Invariant Risk Minimization | https://openreview.net/forum?id=BbNIbVPJ-42 | https://openreview.net/forum?id=BbNIbVPJ-42 | Elan Rosenfeld,Pradeep Kumar Ravikumar,Andrej Risteski | ICLR 2021,Poster | Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain constant. Recently, Arjovsky et al. (2019) proposed Invariant Risk Minimization (IRM), an objective based on this idea for learning deep, invariant features of data which are a complex function of latent variables; many alternatives have subsequently been suggested. However, formal guarantees for all of these works are severely lacking. In this paper, we present the first analysis of classification under the IRM objective—as well as these recently proposed alternatives—under a fairly natural and general model. In the linear case, we show simple conditions under which the optimal solution succeeds or, more often, fails to recover the optimal invariant predictor. We furthermore present the very first results in the non-linear regime: we demonstrate that IRM can fail catastrophically unless the test data is sufficiently similar to the training distribution—this is precisely the issue that it was intended to solve. Thus, in this setting we find that IRM and its alternatives fundamentally do not improve over standard Empirical Risk Minimization. | https://openreview.net/pdf/28dd76f69ec8c36b1e5610943745438b4d6ca498.pdf |
Scaling Symbolic Methods using Gradients for Neural Model Explanation | https://openreview.net/forum?id=V5j-jdoDDP | https://openreview.net/forum?id=V5j-jdoDDP | Subham Sekhar Sahoo,Subhashini Venugopalan,Li Li,Rishabh Singh,Patrick Riley | ICLR 2021,Poster | Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation. In particular, we apply this technique to identify minimal regions in an input that are most relevant for a neural network's prediction. Our approach uses gradient information (based on Integrated Gradients) to focus on a subset of neurons in the first layer, which allows our technique to scale to large networks. The corresponding SMT constraints encode the minimal input mask discovery problem such that after masking the input, the activations of the selected neurons are still above a threshold. After solving for the minimal masks, our approach scores the mask regions to generate a relative ordering of the features within the mask. This produces a saliency map which explains" where a model is looking" when making a prediction. We evaluate our technique on three datasets-MNIST, ImageNet, and Beer Reviews, and demonstrate both quantitatively and qualitatively that the regions generated by our approach are sparser and achieve higher saliency scores compared to the gradient-based methods alone. Code and examples are at - https://github.com/google-research/google-research/tree/master/smug_saliency | https://openreview.net/pdf/d1b9da67f960e7d1f4843fe8b37b4e9cb6cbbc3b.pdf |
Contextual Dropout: An Efficient Sample-Dependent Dropout Module | https://openreview.net/forum?id=ct8_a9h1M | https://openreview.net/forum?id=ct8_a9h1M | XINJIE FAN,Shujian Zhang,Korawat Tanwisuth,Xiaoning Qian,Mingyuan Zhou | ICLR 2021,Poster | Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for prediction. However, the quality of uncertainty estimation is highly dependent on the dropout probabilities. Most current models use the same dropout distributions across all data samples due to its simplicity. Despite the potential gains in the flexibility of modeling uncertainty, sample-dependent dropout, on the other hand, is less explored as it often encounters scalability issues or involves non-trivial model changes. In this paper, we propose contextual dropout with an efficient structural design as a simple and scalable sample-dependent dropout module, which can be applied to a wide range of models at the expense of only slightly increased memory and computational cost. We learn the dropout probabilities with a variational objective, compatible with both Bernoulli dropout and Gaussian dropout. We apply the contextual dropout module to various models with applications to image classification and visual question answering and demonstrate the scalability of the method with large-scale datasets, such as ImageNet and VQA 2.0. Our experimental results show that the proposed method outperforms baseline methods in terms of both accuracy and quality of uncertainty estimation. | https://openreview.net/pdf/e60af0056960e7c5e82ee3d15ad9c6dd3577788d.pdf |
Contrastive Learning with Hard Negative Samples | https://openreview.net/forum?id=CR1XOQ0UTh- | https://openreview.net/forum?id=CR1XOQ0UTh- | Joshua David Robinson,Ching-Yao Chuang,Suvrit Sra,Stefanie Jegelka | ICLR 2021,Poster | We consider the question: how can you sample good negative examples for contrastive learning? We argue that, as with metric learning, learning contrastive representations benefits from hard negative samples (i.e., points that are difficult to distinguish from an anchor point). The key challenge toward using hard negatives is that contrastive methods must remain unsupervised, making it infeasible to adopt existing negative sampling strategies that use label information. In response, we develop a new class of unsupervised methods for selecting hard negative samples where the user can control the amount of hardness. A limiting case of this sampling results in a representation that tightly clusters each class, and pushes different classes as far apart as possible. The proposed method improves downstream performance across multiple modalities, requires only few additional lines of code to implement, and introduces no computational overhead.
| https://openreview.net/pdf/abbdb606c8b0133d5a3f1cb9b239cb75f5530c20.pdf |
Debiasing Concept-based Explanations with Causal Analysis | https://openreview.net/forum?id=6puUoArESGp | https://openreview.net/forum?id=6puUoArESGp | Mohammad Taha Bahadori,David Heckerman | ICLR 2021,Poster | Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts. In this work, we study the problem of the concepts being correlated with confounding information in the features. We propose a new causal prior graph for modeling the impacts of unobserved variables and a method to remove the impact of confounding information and noise using a two-stage regression technique borrowed from the instrumental variable literature. We also model the completeness of the concepts set and show that our debiasing method works when the concepts are not complete. Our synthetic and real-world experiments demonstrate the success of our method in removing biases and improving the ranking of the concepts in terms of their contribution to the explanation of the predictions. | https://openreview.net/pdf/6c474aec6a470f6431f665cd26196adb69dee2cd.pdf |
Risk-Averse Offline Reinforcement Learning | https://openreview.net/forum?id=TBIzh9b5eaz | https://openreview.net/forum?id=TBIzh9b5eaz | Núria Armengol Urpí,Sebastian Curi,Andreas Krause | ICLR 2021,Poster | Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration. Thus, the agent can only use data previously collected by safe policies. While previous work considers optimizing the average performance using offline data, we focus on optimizing a risk-averse criteria, namely the CVaR. In particular, we present the Offline Risk-Averse Actor-Critic (O-RAAC), a model-free RL algorithm that is able to learn risk-averse policies in a fully offline setting. We show that O-RAAC learns policies with higher CVaR than risk-neutral approaches in different robot control tasks. Furthermore, considering risk-averse criteria guarantees distributional robustness of the average performance with respect to particular distribution shifts. We demonstrate empirically that in the presence of natural distribution-shifts, O-RAAC learns policies with good average performance.
| https://openreview.net/pdf/be66503efdccf10359c7112b8c3732b28564db16.pdf |
Parameter Efficient Multimodal Transformers for Video Representation Learning | https://openreview.net/forum?id=6UdQLhqJyFD | https://openreview.net/forum?id=6UdQLhqJyFD | Sangho Lee,Youngjae Yu,Gunhee Kim,Thomas Breuel,Jan Kautz,Yale Song | ICLR 2021,Poster | The recent success of Transformers in the language domain has motivated adapting it to a multimodal setting, where a new visual model is trained in tandem with an already pretrained language model. However, due to the excessive memory requirements from Transformers, existing work typically fixes the language model and train only the vision module, which limits its ability to learn cross-modal information in an end-to-end manner. In this work, we focus on reducing the parameters of multimodal Transformers in the context of audio-visual video representation learning. We alleviate the high memory requirement by sharing the parameters of Transformers across layers and modalities; we decompose the Transformer into modality-specific and modality-shared parts so that the model learns the dynamics of each modality both individually and together, and propose a novel parameter sharing scheme based on low-rank approximation. We show that our approach reduces parameters of the Transformers up to 97%, allowing us to train our model end-to-end from scratch. We also propose a negative sampling approach based on an instance similarity measured on the CNN embedding space that our model learns together with the Transformers. To demonstrate our approach, we pretrain our model on 30-second clips (480 frames) from Kinetics-700 and transfer it to audio-visual classification tasks. | https://openreview.net/pdf/2cccd8332d73cd6ac7e33027594d30f19af464d5.pdf |
Tradeoffs in Data Augmentation: An Empirical Study | https://openreview.net/forum?id=ZcKPWuhG6wy | https://openreview.net/forum?id=ZcKPWuhG6wy | Raphael Gontijo-Lopes,Sylvia Smullin,Ekin Dogus Cubuk,Ethan Dyer | ICLR 2021,Poster | Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of distribution shift or augmentation diversity. Inspired by these, we conduct an empirical study to quantify how data augmentation improves model generalization. We introduce two interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two. | https://openreview.net/pdf/3d2fc5aa6c81e18581fdf14b971478263093ec81.pdf |
Concept Learners for Few-Shot Learning | https://openreview.net/forum?id=eJIJF3-LoZO | https://openreview.net/forum?id=eJIJF3-LoZO | Kaidi Cao,Maria Brbic,Jure Leskovec | ICLR 2021,Poster | Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving 6-15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model's predictions. | https://openreview.net/pdf/4502f92e9d047dea9aa42deceb3ff6b44a74942e.pdf |
Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics | https://openreview.net/forum?id=q8qLAbQBupm | https://openreview.net/forum?id=q8qLAbQBupm | Daniel Kunin,Javier Sagastuy-Brena,Surya Ganguli,Daniel LK Yamins,Hidenori Tanaka | ICLR 2021,Poster | Understanding the dynamics of neural network parameters during training is one of the key challenges in building a theoretical foundation for deep learning. A central obstacle is that the motion of a network in high-dimensional parameter space undergoes discrete finite steps along complex stochastic gradients derived from real-world datasets. We circumvent this obstacle through a unifying theoretical framework based on intrinsic symmetries embedded in a network's architecture that are present for any dataset. We show that any such symmetry imposes stringent geometric constraints on gradients and Hessians, leading to an associated conservation law in the continuous-time limit of stochastic gradient descent (SGD), akin to Noether's theorem in physics. We further show that finite learning rates used in practice can actually break these symmetry induced conservation laws. We apply tools from finite difference methods to derive modified gradient flow, a differential equation that better approximates the numerical trajectory taken by SGD at finite learning rates. We combine modified gradient flow with our framework of symmetries to derive exact integral expressions for the dynamics of certain parameter combinations. We empirically validate our analytic expressions for learning dynamics on VGG-16 trained on Tiny ImageNet. Overall, by exploiting symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset. | https://openreview.net/pdf/5abc5ac75318bd0ee0b7d7d6a805c51471a672e0.pdf |
Fair Mixup: Fairness via Interpolation | https://openreview.net/forum?id=DNl5s5BXeBn | https://openreview.net/forum?id=DNl5s5BXeBn | Ching-Yao Chuang,Youssef Mroueh | ICLR 2021,Poster | Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are satisfied during training, they might not generalize at evaluation time. To improve the generalizability of fair classifiers, we propose fair mixup, a new data augmentation strategy for imposing the fairness constraint. In particular, we show that fairness can be achieved by regularizing the models on paths of interpolated samples between the groups. We use mixup, a powerful data augmentation strategy to generate these interpolates. We analyze fair mixup and empirically show that it ensures a better generalization for both accuracy and fairness measurement in tabular, vision, and language benchmarks. | https://openreview.net/pdf/26fcabb2173a9b48339615540d74ed1bc1c6281e.pdf |
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds | https://openreview.net/forum?id=UwGY2qjqoLD | https://openreview.net/forum?id=UwGY2qjqoLD | Bogdan Georgiev,Lukas Franken,Mayukh Mukherjee | ICLR 2021,Poster | In the present work we study classifiers' decision boundaries via Brownian motion processes in ambient data space and associated probabilistic techniques. Intuitively, our ideas correspond to placing a heat source at the decision boundary and observing how effectively the sample points warm up. We are largely motivated by the search for a soft measure that sheds further light on the decision boundary's geometry. En route, we bridge aspects of potential theory and geometric analysis (Maz'ya 2011, Grigor'Yan and Saloff-Coste 2002) with active fields of ML research such as adversarial examples and generalization bounds. First, we focus on the geometric behavior of decision boundaries in the light of adversarial attack/defense mechanisms. Experimentally, we observe a certain capacitory trend over different adversarial defense strategies: decision boundaries locally become flatter as measured by isoperimetric inequalities (Ford et al 2019); however, our more sensitive heat-diffusion metrics extend this analysis and further reveal that some non-trivial geometry invisible to plain distance-based methods is still preserved. Intuitively, we provide evidence that the decision boundaries nevertheless retain many persistent "wiggly and fuzzy" regions on a finer scale.
Second, we show how Brownian hitting probabilities translate to soft generalization bounds which are in turn connected to compression and noise stability (Arora et al 2018), and these bounds are significantly stronger if the decision boundary has controlled geometric features. | https://openreview.net/pdf/e8a429ae487de8dda95c91d6c5401966de30ba95.pdf |
Influence Functions in Deep Learning Are Fragile | https://openreview.net/forum?id=xHKVVHGDOEk | https://openreview.net/forum?id=xHKVVHGDOEk | Samyadeep Basu,Phil Pope,Soheil Feizi | ICLR 2021,Poster | Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty estimation. A commonly-used (first-order) influence function can be implemented efficiently as a post-hoc method requiring access only to the gradients and Hessian of the model. For linear models, influence functions are well-defined due to the convexity of the underlying loss function and are generally accurate even across difficult settings where model changes are fairly large such as estimating group influences. Influence functions, however, are not well-understood in the context of deep learning with non-convex loss functions. In this paper, we provide a comprehensive and large-scale empirical study of successes and failures of influence functions in neural network models trained on datasets such as Iris, MNIST, CIFAR-10 and ImageNet. Through our extensive experiments, we show that the network architecture, its depth and width, as well as the extent of model parameterization and regularization techniques have strong effects in the accuracy of influence functions. In particular, we find that (i) influence estimates are fairly accurate for shallow networks, while for deeper networks the estimates are often erroneous; (ii) for certain network architectures and datasets, training with weight-decay regularization is important to get high-quality influence estimates; and (iii) the accuracy of influence estimates can vary significantly depending on the examined test points. These results suggest that in general influence functions in deep learning are fragile and call for developing improved influence estimation methods to mitigate these issues in non-convex setups. | https://openreview.net/pdf/5f4c0d6ade226a87db8c80975d4487e28ec11d9e.pdf |
Few-Shot Learning via Learning the Representation, Provably | https://openreview.net/forum?id=pW2Q2xLwIMD | https://openreview.net/forum?id=pW2Q2xLwIMD | Simon Shaolei Du,Wei Hu,Sham M. Kakade,Jason D. Lee,Qi Lei | ICLR 2021,Poster | This paper studies few-shot learning via representation learning, where one uses $T$ source tasks with $n_1$ data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only $n_2 (\ll n_1)$ data. Specifically, we focus on the setting where there exists a good common representation between source and target, and our goal is to understand how much a sample size reduction is possible. First, we study the setting where this common representation is low-dimensional and provide a risk bound of $\tilde{O}(\frac{dk}{n_1T} + \frac{k}{n_2})$ on the target task for the linear representation class; here $d$ is the ambient input dimension and $k (\ll d)$ is the dimension of the representation. This result bypasses the $\Omega(\frac{1}{T})$ barrier under the i.i.d. task assumption, and can capture the desired property that all $n_1T$ samples from source tasks can be \emph{pooled} together for representation learning. We further extend this result to handle a general representation function class and obtain a similar result. Next, we consider the setting where the common representation may be high-dimensional but is capacity-constrained (say in norm); here, we again demonstrate the advantage of representation learning in both high-dimensional linear regression and neural networks, and show that representation learning can fully utilize all $n_1T$ samples from source tasks. | https://openreview.net/pdf/d552a527585afe0600e439411a5e9152ce459182.pdf |
Self-Supervised Learning of Compressed Video Representations | https://openreview.net/forum?id=jMPcEkJpdD | https://openreview.net/forum?id=jMPcEkJpdD | Youngjae Yu,Sangho Lee,Gunhee Kim,Yale Song | ICLR 2021,Poster | Self-supervised learning of video representations has received great attention. Existing methods typically require frames to be decoded before being processed, which increases compute and storage requirements and ultimately hinders large-scale training. In this work, we propose an efficient self-supervised approach to learn video representations by eliminating the expensive decoding step. We use a three-stream video architecture that encodes I-frames and P-frames of a compressed video. Unlike existing approaches that encode I-frames and P-frames individually, we propose to jointly encode them by establishing bidirectional dynamic connections across streams. To enable self-supervised learning, we propose two pretext tasks that leverage the multimodal nature (RGB, motion vector, residuals) and the internal GOP structure of compressed videos. The first task asks our network to predict zeroth-order motion statistics in a spatio-temporal pyramid; the second task asks correspondence types between I-frames and P-frames after applying temporal transformations. We show that our approach achieves competitive performance on compressed video recognition both in supervised and self-supervised regimes.
| https://openreview.net/pdf/32a283d43ef9e69a3039580759776862664a9146.pdf |
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis | https://openreview.net/forum?id=Ig53hpHxS4 | https://openreview.net/forum?id=Ig53hpHxS4 | Rafael Valle,Kevin J. Shih,Ryan Prenger,Bryan Catanzaro | ICLR 2021,Poster | In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with style transfer and speech variation. Flowtron borrows insights from Autoregressive Flows and revamps Tacotron 2 in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized by maximizing the likelihood of the training data, which makes training simple and stable. Flowtron learns an invertible mapping of data to a latent space that can be used to modulate many aspects of speech synthesis (timbre, expressivity, accent). Our mean opinion scores (MOS) show that Flowtron matches state-of-the-art TTS models in terms of speech quality. We provide results on speech variation, interpolation over time between samples and style transfer between seen and unseen speakers. Code and pre-trained models are publicly available at \href{https://github.com/NVIDIA/flowtron}{https://github.com/NVIDIA/flowtron}. | https://openreview.net/pdf/f022be71a045af955f0b061b9fb50186c102f407.pdf |
R-GAP: Recursive Gradient Attack on Privacy | https://openreview.net/forum?id=RSU17UoKfJF | https://openreview.net/forum?id=RSU17UoKfJF | Junyi Zhu,Matthew B. Blaschko | ICLR 2021,Poster | Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data. Many such frameworks only ask collaborators to share their local update of a common model, i.e. gradients with respect to locally stored data, instead of exposing their raw data to other collaborators. However, recent optimization-based gradient attacks show that raw data can often be accurately recovered from gradients. It has been shown that minimizing the Euclidean distance between true gradients and those calculated from estimated data is often effective in fully recovering private data. However, there is a fundamental lack of theoretical understanding of how and when gradients can lead to unique recovery of original data. Our research fills this gap by providing a closed-form recursive procedure to recover data from gradients in deep neural networks. We name it Recursive Gradient Attack on Privacy (R-GAP). Experimental results demonstrate that R-GAP works as well as or even better than optimization-based approaches at a fraction of the computation under certain conditions. Additionally, we propose a Rank Analysis method, which can be used to estimate the risk of gradient attacks inherent in certain network architectures, regardless of whether an optimization-based or closed-form-recursive attack is used. Experimental results demonstrate the utility of the rank analysis towards improving the network's security. Source code is available for download from https://github.com/JunyiZhu-AI/R-GAP. | https://openreview.net/pdf/943e8621d1ff1fb8b9daa8cc68876c327966099c.pdf |
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients | https://openreview.net/forum?id=iQQK02mxVIT | https://openreview.net/forum?id=iQQK02mxVIT | Jing An,Lexing Ying,Yuhua Zhu | ICLR 2021,Poster | A data set sampled from a certain population is biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying proportions. Training machine learning models on biased data sets requires correction techniques to compensate for the bias. We consider two commonly-used techniques, resampling and reweighting, that rebalance the proportions of the subgroups to maintain the desired objective function. Though statistically equivalent, it has been observed that resampling outperforms reweighting when combined with stochastic gradient algorithms. By analyzing illustrative examples, we explain the reason behind this phenomenon using tools from dynamical stability and stochastic asymptotics. We also present experiments from regression, classification, and off-policy prediction to demonstrate that this is a general phenomenon. We argue that it is imperative to consider the objective function design and the optimization algorithm together while addressing the sampling bias.
| https://openreview.net/pdf/0fa836fe2e9df3b7a0bc598d12aa906302c5bf9b.pdf |
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach | https://openreview.net/forum?id=2VXyy9mIyU3 | https://openreview.net/forum?id=2VXyy9mIyU3 | Hao Cheng,Zhaowei Zhu,Xingyu Li,Yifei Gong,Xing Sun,Yang Liu | ICLR 2021,Poster | Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent of features. Practically, annotations errors tend to be instance-dependent and often depend on the difficulty levels of recognizing a certain task. Applying existing results from instance-independent settings would require a significant amount of estimation of noise rates. Therefore, providing theoretically rigorous solutions for learning with instance-dependent label noise remains a challenge. In this paper, we propose CORES$^{2}$ (COnfidence REgularized Sample Sieve), which progressively sieves out corrupted examples. The implementation of CORES$^{2}$ does not require specifying noise rates and yet we are able to provide theoretical guarantees of CORES$^{2}$ in filtering out the corrupted examples. This high-quality sample sieve allows us to treat clean examples and the corrupted ones separately in training a DNN solution, and such a separation is shown to be advantageous in the instance-dependent noise setting. We demonstrate the performance of CORES$^{2}$ on CIFAR10 and CIFAR100 datasets with synthetic instance-dependent label noise and Clothing1M with real-world human noise. As of independent interests, our sample sieve provides a generic machinery for anatomizing noisy datasets and provides a flexible interface for various robust training techniques to further improve the performance. Code is available at https://github.com/UCSC-REAL/cores. | https://openreview.net/pdf/d1f9063b86dd15432acc0f429ea3d22cbb41ae74.pdf |
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders | https://openreview.net/forum?id=43VKWxg_Sqr | https://openreview.net/forum?id=43VKWxg_Sqr | Kangle Deng,Aayush Bansal,Deva Ramanan | ICLR 2021,Poster | We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribution of the training set. We use exemplar autoencoders to learn the voice, stylistic prosody, and visual appearance of a specific target exemplar speech. In contrast to existing methods, the proposed approach can be easily extended to an arbitrarily large number of speakers and styles using only 3 minutes of target audio-video data, without requiring any training data for the input speaker. To do so, we learn audiovisual bottleneck representations that capture the structured linguistic content of speech. We outperform prior approaches on both audio and video synthesis.
| https://openreview.net/pdf/6310fc99e7f1359a15e30437011f7577dab60ff1.pdf |
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy | https://openreview.net/forum?id=pqZV_srUVmK | https://openreview.net/forum?id=pqZV_srUVmK | Zuyue Fu,Zhuoran Yang,Zhaoran Wang | ICLR 2021,Poster | We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-critic employ bi-level or two-timescale updates, we focus on the more practical single-timescale setting, where the actor and critic are updated simultaneously. Specifically, in each iteration, the critic update is obtained by applying the Bellman evaluation operator only once while the actor is updated in the policy gradient direction computed using the critic. Moreover, we consider two function approximation settings where both the actor and critic are represented by linear or deep neural networks. For both cases, we prove that the actor sequence converges to a globally optimal policy at a sublinear $O(K^{-1/2})$ rate, where $K$ is the number of iterations. To the best of our knowledge, we establish the rate of convergence and global optimality of single-timescale actor-critic with linear function approximation for the first time. Moreover, under the broader scope of policy optimization with nonlinear function approximation, we prove that actor-critic with deep neural network finds the globally optimal policy at a sublinear rate for the first time. | https://openreview.net/pdf/23d733db99e61b484d06180f5dfbb470789b7ff0.pdf |
Wandering within a world: Online contextualized few-shot learning | https://openreview.net/forum?id=oZIvHV04XgC | https://openreview.net/forum?id=oZIvHV04XgC | Mengye Ren,Michael Louis Iuzzolino,Michael Curtis Mozer,Richard Zemel | ICLR 2021,Poster | We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and instead models are evaluated online while learning novel classes. As in the real world, where the presence of spatiotemporal context helps us retrieve learned skills in the past, our online few-shot learning setting also features an underlying context that changes throughout time. Object classes are correlated within a context and inferring the correct context can lead to better performance. Building upon this setting, we propose a new few-shot learning dataset based on large scale indoor imagery that mimics the visual
experience of an agent wandering within a world. Furthermore, we convert popular few-shot learning approaches into online versions and we also propose a new model that can make use of spatiotemporal contextual information from the recent past. | https://openreview.net/pdf/c7d39970b7f4089067f9662d8cebe4f73c71d1bb.pdf |
Neural Delay Differential Equations | https://openreview.net/forum?id=Q1jmmQz72M2 | https://openreview.net/forum?id=Q1jmmQz72M2 | Qunxi Zhu,Yao Guo,Wei Lin | ICLR 2021,Poster | Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here we propose a new class of continuous-depth neural networks with delay, named as Neural Delay Differential Equations (NDDEs), and, for computing the corresponding gradients, we use the adjoint sensitivity method to obtain the delayed dynamics of the adjoint. Since the differential equations with delays are usually seen as dynamical systems of infinite dimension possessing more fruitful dynamics, the NDDEs, compared to the NODEs, own a stronger capacity of nonlinear representations. Indeed, we analytically validate that the NDDEs are of universal approximators, and further articulate an extension of the NDDEs, where the initial function of the NDDEs is supposed to satisfy ODEs. More importantly, we use several illustrative examples to demonstrate the outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value. More precisely, (1) we successfully model the delayed dynamics where the trajectories in the lower-dimensional phase space could be mutually intersected, while the traditional NODEs without any argumentation are not directly applicable for such modeling, and (2) we achieve lower loss and higher accuracy not only for the data produced synthetically by complex models but also for the real-world image datasets, i.e., CIFAR10, MNIST and SVHN. Our results on the NDDEs reveal that appropriately articulating the elements of dynamical systems into the network design is truly beneficial to promoting the network performance. | https://openreview.net/pdf/39dbf04903dd5453991dacd8dcb2333de44b3a6e.pdf |
Learning Parametrised Graph Shift Operators | https://openreview.net/forum?id=0OlrLvrsHwQ | https://openreview.net/forum?id=0OlrLvrsHwQ | George Dasoulas,Johannes F. Lutzeyer,Michalis Vazirgiannis | ICLR 2021,Poster | In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian matrices and their normalisations. In this paper, a novel parametrised GSO (PGSO) is proposed, where specific parameter values result in the most commonly used GSOs and message-passing operators in graph neural network (GNN) frameworks. The PGSO is suggested as a replacement of the standard GSOs that are used in state-of-the-art GNN architectures and the optimisation of the PGSO parameters is seamlessly included in the model training. It is proved that the PGSO has real eigenvalues and a set of real eigenvectors independent of the parameter values and spectral bounds on the PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the graph structure in a study on stochastic blockmodel networks, where they are found to automatically replicate the GSO regularisation found in the literature. On several real-world datasets the accuracy of state-of-the-art GNN architectures is improved by the inclusion of the PGSO in both node- and graph-classification tasks. | https://openreview.net/pdf/e6905a755d143d191885c3b5fef08eb1af58ebb1.pdf |
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients | https://openreview.net/forum?id=6FqKiVAdI3Y | https://openreview.net/forum?id=6FqKiVAdI3Y | Yihan Wang,Beining Han,Tonghan Wang,Heng Dong,Chongjie Zhang | ICLR 2021,Poster | Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art multi-agent value-based approaches. In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP). This method introduces the idea of value function decomposition into the multi-agent actor-critic framework. Based on this idea, DOP supports efficient off-policy learning and addresses the issue of centralized-decentralized mismatch and credit assignment in both discrete and continuous action spaces. We formally show that DOP critics have sufficient representational capability to guarantee convergence. In addition, empirical evaluations on the StarCraft II micromanagement benchmark and multi-agent particle environments demonstrate that DOP outperforms both state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms. Demonstrative videos are available at https://sites.google.com/view/dop-mapg/. | https://openreview.net/pdf/3f81a57de106341a8eae39f9a550980090df7241.pdf |
Unsupervised Discovery of 3D Physical Objects from Video | https://openreview.net/forum?id=lf7st0bJIA5 | https://openreview.net/forum?id=lf7st0bJIA5 | Yilun Du,Kevin A. Smith,Tomer Ullman,Joshua B. Tenenbaum,Jiajun Wu | ICLR 2021,Poster | We study the problem of unsupervised physical object discovery. While existing frameworks aim to decompose scenes into 2D segments based off each object's appearance, we explore how physics, especially object interactions, facilitates disentangling of 3D geometry and position of objects from video, in an unsupervised manner. Drawing inspiration from developmental psychology, our Physical Object Discovery Network (POD-Net) uses both multi-scale pixel cues and physical motion cues to accurately segment observable and partially occluded objects of varying sizes, and infer properties of those objects. Our model reliably segments objects on both synthetic and real scenes. The discovered object properties can also be used to reason about physical events. | https://openreview.net/pdf/b246a3d6a8c606639649d7945c479176a1f965c7.pdf |
Exploring Balanced Feature Spaces for Representation Learning | https://openreview.net/forum?id=OqtLIabPTit | https://openreview.net/forum?id=OqtLIabPTit | Bingyi Kang,Yu Li,Sa Xie,Zehuan Yuan,Jiashi Feng | ICLR 2021,Poster | Existing self-supervised learning (SSL) methods are mostly applied for training representation models from artificially balanced datasets (e.g., ImageNet). It is unclear how well they will perform in the practical scenarios where datasets are often imbalanced w.r.t. the classes. Motivated by this question, we conduct a series of studies on the performance of self-supervised contrastive learning and supervised learning methods over multiple datasets where training instance distributions vary from a balanced one to a long-tailed one. Our findings are quite intriguing. Different from supervised methods with large performance drop, the self-supervised contrastive learning methods perform stably well even when the datasets are heavily imbalanced. This motivates us to explore the balanced feature spaces learned by contrastive learning, where the feature representations present similar linear separability w.r.t. all the classes. Our further experiments reveal that a representation model generating a balanced feature space can generalize better than that yielding an imbalanced one across multiple settings. Inspired by these insights, we develop a novel representation learning method, called $k$-positive contrastive learning. It effectively combines strengths of the supervised method and the contrastive learning method to learn representations that are both discriminative and balanced. Extensive experiments demonstrate its superiority on multiple recognition tasks. Remarkably, it achieves new state-of-the-art on challenging long-tailed recognition benchmarks. Code and models will be released. | https://openreview.net/pdf/a6e3728ce8efd94d339778f7a7bd85042c5df475.pdf |
Auxiliary Learning by Implicit Differentiation | https://openreview.net/forum?id=n7wIfYPdVet | https://openreview.net/forum?id=n7wIfYPdVet | Aviv Navon,Idan Achituve,Haggai Maron,Gal Chechik,Ethan Fetaya | ICLR 2021,Poster | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest.
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. This network can learn non-linear interactions between tasks. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task. We evaluate AuxiLearn in a series of tasks and domains, including image segmentation and learning with attributes in the low data regime, and find that it consistently outperforms competing methods. | https://openreview.net/pdf/2571967094ddf29d39923e16c68d6ddb7f5bb72e.pdf |
On the Bottleneck of Graph Neural Networks and its Practical Implications | https://openreview.net/forum?id=i80OPhOCVH2 | https://openreview.net/forum?id=i80OPhOCVH2 | Uri Alon,Eran Yahav | ICLR 2021,Poster | Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph.
We propose a new explanation for this problem: GNNs are susceptible to a bottleneck when aggregating messages across a long path. This bottleneck causes the over-squashing of exponentially growing information into fixed-size vectors.
As a result, GNNs fail to propagate messages originating from distant nodes and perform poorly when the prediction task depends on long-range interaction.
In this paper, we highlight the inherent problem of over-squashing in GNNs:
we demonstrate that the bottleneck hinders popular GNNs from fitting long-range signals in the training data;
we further show that GNNs that absorb incoming edges equally, such as GCN and GIN, are more susceptible to over-squashing than GAT and GGNN;
finally, we show that prior work, which extensively tuned GNN models of long-range problems, suffers from over-squashing, and that breaking the bottleneck improves their state-of-the-art results without any tuning or additional weights.
Our code is available at https://github.com/tech-srl/bottleneck/ . | https://openreview.net/pdf/d8fb680febbb7eb04a6ffa0380ec045684628ebc.pdf |
The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods | https://openreview.net/forum?id=L7WD8ZdscQ5 | https://openreview.net/forum?id=L7WD8ZdscQ5 | Wei Tao,Sheng Long,Gaowei Wu,Qing Tao | ICLR 2021,Poster | The adaptive stochastic gradient descent (SGD) with momentum has been widely adopted in deep learning as well as convex optimization. In practice, the last iterate is commonly used as the final solution. However, the available regret analysis and the setting of constant momentum parameters only guarantee the optimal convergence of the averaged solution. In this paper, we fill this theory-practice gap by investigating the convergence of the last iterate (referred to as {\it individual convergence}), which is a more difficult task than convergence analysis of the averaged solution. Specifically, in the constrained convex cases, we prove that the adaptive Polyak's Heavy-ball (HB) method, in which the step size is only updated using the exponential moving average strategy, attains an individual convergence rate of $O(\frac{1}{\sqrt{t}})$, as opposed to that of $O(\frac{\log t}{\sqrt {t}})$ of SGD, where $t$ is the number of iterations. Our new analysis not only shows how the HB momentum and its time-varying weight help us to achieve the acceleration in convex optimization but also gives valuable hints how the momentum parameters should be scheduled in deep learning. Empirical results validate the correctness of our convergence analysis in optimizing convex functions and demonstrate the improved performance of the adaptive HB methods in training deep networks. | https://openreview.net/pdf/88887f8c02852ef3a9f82b1ae8a010345d221175.pdf |
Entropic gradient descent algorithms and wide flat minima | https://openreview.net/forum?id=xjXg0bnoDmS | https://openreview.net/forum?id=xjXg0bnoDmS | Fabrizio Pittorino,Carlo Lucibello,Christoph Feinauer,Gabriele Perugini,Carlo Baldassi,Elizaveta Demyanenko,Riccardo Zecchina | ICLR 2021,Poster | The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, Entropy-SGD and Replicated-SGD, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet). | https://openreview.net/pdf/0b359e4e5d157768feb17f39a57619c3f110d21f.pdf |
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning | https://openreview.net/forum?id=XLfdzwNKzch | https://openreview.net/forum?id=XLfdzwNKzch | Myeongjang Pyeon,Jihwan Moon,Taeyoung Hahn,Gunhee Kim | ICLR 2021,Poster | Backward locking and update locking are well-known sources of inefficiency in backpropagation that prevent from concurrently updating layers. Several works have recently suggested using local error signals to train network blocks asynchronously to overcome these limitations. However, they often require numerous iterations of trial-and-error to find the best configuration for local training, including how to decouple network blocks and which auxiliary networks to use for each block. In this work, we propose a differentiable search algorithm named SEDONA to automate this process. Experimental results show that our algorithm can consistently discover transferable decoupled architectures for VGG and ResNet variants, and significantly outperforms the ones trained with end-to-end backpropagation and other state-of-the-art greedy-leaning methods in CIFAR-10, Tiny-ImageNet and ImageNet. | https://openreview.net/pdf/0b88f9a4f997ec52f81dce9a0410a6b5646e18a8.pdf |
not-MIWAE: Deep Generative Modelling with Missing not at Random Data | https://openreview.net/forum?id=tu29GQT0JFy | https://openreview.net/forum?id=tu29GQT0JFy | Niels Bruun Ipsen,Pierre-Alexandre Mattei,Jes Frellsen | ICLR 2021,Poster | When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g.~self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable. | https://openreview.net/pdf/cd42c1e09d98ef209caa6b63d5a67a5273108128.pdf |
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss | https://openreview.net/forum?id=9G5MIc-goqB | https://openreview.net/forum?id=9G5MIc-goqB | Mingyang Yi,Lu Hou,Lifeng Shang,Xin Jiang,Qun Liu,Zhi-Ming Ma | ICLR 2021,Poster | Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the model. To address this, for the augmented samples from the same training example, we propose to assign different weights to them. We construct the maximal expected loss which is the supremum over any reweighted loss on augmented samples. Inspired by adversarial training, we minimize this maximal expected loss (MMEL) and obtain a simple and interpretable closed-form solution: more attention should be paid to augmented samples with large loss values (i.e., harder examples). Minimizing this maximal expected loss enables the model to perform well under any reweighting strategy. The proposed method can generally be applied on top of any data augmentation methods. Experiments are conducted on both natural language understanding tasks with token-level data augmentation, and image classification tasks with commonly-used image augmentation techniques like random crop and horizontal flip. Empirical results show that the proposed method improves the generalization performance of the model. | https://openreview.net/pdf/e4c9206df0bf95f0a614a0ac2cc2acd08f5125ce.pdf |
Learning to Represent Action Values as a Hypergraph on the Action Vertices | https://openreview.net/forum?id=Xv_s64FiXTv | https://openreview.net/forum?id=Xv_s64FiXTv | Arash Tavakoli,Mehdi Fatemi,Petar Kormushev | ICLR 2021,Poster | Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facilitate action-value estimation. While advances in deep learning have seamlessly driven progress in learning state representations, given the specificity of the notion of agency to RL, little attention has been paid to learning action representations. We conjecture that leveraging the combinatorial structure of multi-dimensional action spaces is a key ingredient for learning good representations of action. To test this, we set forth the action hypergraph networks framework---a class of functions for learning action representations in multi-dimensional discrete action spaces with a structural inductive bias. Using this framework we realise an agent class based on a combination with deep Q-networks, which we dub hypergraph Q-networks. We show the effectiveness of our approach on a myriad of domains: illustrative prediction problems under minimal confounding effects, Atari 2600 games, and discretised physical control benchmarks. | https://openreview.net/pdf/1ffba349384a4d39e139b3deafa3e80e587e2dc1.pdf |
Shapley Explanation Networks | https://openreview.net/forum?id=vsU0efpivw | https://openreview.net/forum?id=vsU0efpivw | Rui Wang,Xiaoqian Wang,David I. Inouye | ICLR 2021,Poster | Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on post-hoc Shapley explanations, which can be computationally demanding due to its exponential time complexity and preclude model regularization based on Shapley explanations during training. Thus, we propose to incorporate Shapley values themselves as latent representations in deep models thereby making Shapley explanations first-class citizens in the modeling paradigm. This intrinsic explanation approach enables layer-wise explanations, explanation regularization of the model during training, and fast explanation computation at test time. We define the Shapley transform that transforms the input into a Shapley representation given a specific function. We operationalize the Shapley transform as a neural network module and construct both shallow and deep networks, called ShapNets, by composing Shapley modules. We prove that our Shallow ShapNets compute the exact Shapley values and our Deep ShapNets maintain the missingness and accuracy properties of Shapley values. We demonstrate on synthetic and real-world datasets that our ShapNets enable layer-wise Shapley explanations, novel Shapley regularizations during training, and fast computation while maintaining reasonable performance. Code is available at https://github.com/inouye-lab/ShapleyExplanationNetworks. | https://openreview.net/pdf/12770a255a18bc6c25ce69bdda082fd0d7a8cc87.pdf |
NBDT: Neural-Backed Decision Tree | https://openreview.net/forum?id=mCLVeEpplNE | https://openreview.net/forum?id=mCLVeEpplNE | Alvin Wan,Lisa Dunlap,Daniel Ho,Jihan Yin,Scott Lee,Suzanne Petryk,Sarah Adel Bargal,Joseph E. Gonzalez | ICLR 2021,Poster | Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at https://github.com/alvinwan/neural-backed-decision-trees. | https://openreview.net/pdf/9083652f9da499bb55946d63a399c08093258e1c.pdf |
Denoising Diffusion Implicit Models | https://openreview.net/forum?id=St1giarCHLP | https://openreview.net/forum?id=St1giarCHLP | Jiaming Song,Chenlin Meng,Stefano Ermon | ICLR 2021,Poster | Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a particular Markovian diffusion process. We generalize DDPMs via a class of non-Markovian diffusion processes that lead to the same training objective. These non-Markovian processes can correspond to generative processes that are deterministic, giving rise to implicit models that produce high quality samples much faster. We empirically demonstrate that DDIMs can produce high quality samples $10 \times$ to $50 \times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, perform semantically meaningful image interpolation directly in the latent space, and reconstruct observations with very low error. | https://openreview.net/pdf/d01650b6472e18084dcf272952f04a11ffbd6065.pdf |
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding | https://openreview.net/forum?id=qrwe7XHTmYb | https://openreview.net/forum?id=qrwe7XHTmYb | Dmitry Lepikhin,HyoukJoong Lee,Yuanzhong Xu,Dehao Chen,Orhan Firat,Yanping Huang,Maxim Krikun,Noam Shazeer,Zhifeng Chen | ICLR 2021,Poster | Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost,ease of programming, and efficient implementation on parallel devices. In this paper we demonstrate conditional computation as a remedy to the above mentioned impediments, and demonstrate its efficacy and utility. We make extensive use of GShard, a module composed of a set of lightweight annotation APIs and an extension to the XLA compiler to enable large scale models with up to trillions of parameters. GShard and conditional computation enable us to scale up multilingual neural machine translation Transformer model with Sparsely-Gated Mixture-of-Experts. We demonstrate that such a giant model with 600 billion parameters can efficiently be trained on 2048 TPU v3 cores in 4 days to achieve far superior quality for translation from 100 languages to English compared to the prior art. | https://openreview.net/pdf/cdb90e31da8446076492c5aef0c8c6ae35dd472a.pdf |
What Should Not Be Contrastive in Contrastive Learning | https://openreview.net/forum?id=CZ8Y3NzuVzO | https://openreview.net/forum?id=CZ8Y3NzuVzO | Tete Xiao,Xiaolong Wang,Alexei A Efros,Trevor Darrell | ICLR 2021,Poster | Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. However, these methods implicitly assume a particular set of representational invariances (e.g., invariance to color), and can perform poorly when a downstream task violates this assumption (e.g., distinguishing red vs. yellow cars). We introduce a contrastive learning framework which does not require prior knowledge of specific, task-dependent invariances. Our model learns to capture varying and invariant factors for visual representations by constructing separate embedding spaces, each of which is invariant to all but one augmentation. We use a multi-head network with a shared backbone which captures information across each augmentation and alone outperforms all baselines on downstream tasks. We further find that the concatenation of the invariant and varying spaces performs best across all tasks we investigate, including coarse-grained, fine-grained, and few-shot downstream classification tasks, and various data corruptions. | https://openreview.net/pdf/2b5543cc1e932e02f5311e6077b7743dd8c446f5.pdf |
On Data-Augmentation and Consistency-Based Semi-Supervised Learning | https://openreview.net/forum?id=7FNqrcPtieT | https://openreview.net/forum?id=7FNqrcPtieT | Atin Ghosh,Alexandre H. Thiery | ICLR 2021,Poster | Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the Pi-model, temporal ensembling, the mean teacher, or the virtual adversarial training, achieve the state of the art results in several SSL tasks. These methods can typically reach performances that are comparable to their fully supervised counterparts while using only a fraction of labelled examples. Despite these methodological advances, the understanding of these methods is still relatively limited. To make progress, we analyse (variations of) the Pi-model in settings where analytically tractable results can be obtained. We establish links with Manifold Tangent Classifiers and demonstrate that the quality of the perturbations is key to obtaining reasonable SSL performances. Furthermore, we propose a simple extension of the Hidden Manifold Model that naturally incorporates data-augmentation schemes and offers a tractable framework for understanding SSL methods. | https://openreview.net/pdf/b336e63b29c715b76018ba42cb29f2592569ab56.pdf |
Diverse Video Generation using a Gaussian Process Trigger | https://openreview.net/forum?id=Qm7R_SdqTpT | https://openreview.net/forum?id=Qm7R_SdqTpT | Gaurav Shrivastava,Abhinav Shrivastava | ICLR 2021,Poster | Generating future frames given a few context (or past) frames is a challenging task. It requires modeling the temporal coherence of videos as well as multi-modality in terms of diversity in the potential future states. Current variational approaches for video generation tend to marginalize over multi-modal future outcomes. Instead, we propose to explicitly model the multi-modality in the future outcomes and leverage it to sample diverse futures. Our approach, Diverse Video Generator, uses a GP to learn priors on future states given the past and maintains a probability distribution over possible futures given a particular sample. We leverage the changes in this distribution over time to control the sampling of diverse future states by estimating the end of on-going sequences. In particular, we use the variance of GP over the output function space to trigger a change in the action sequence. We achieve state-of-the-art results on diverse future frame generation in terms of reconstruction quality and diversity of the generated sequences. | https://openreview.net/pdf/fa17e9ba0b11fb7c0b424feda300e370d82df3eb.pdf |
Monotonic Kronecker-Factored Lattice | https://openreview.net/forum?id=0pxiMpCyBtr | https://openreview.net/forum?id=0pxiMpCyBtr | William Taylor Bakst,Nobuyuki Morioka,Erez Louidor | ICLR 2021,Poster | It is computationally challenging to learn flexible monotonic functions that guarantee model behavior and provide interpretability beyond a few input features, and in a time where minimizing resource use is increasingly important, we must be able to learn such models that are still efficient. In this paper we show how to effectively and efficiently learn such functions using Kronecker-Factored Lattice ($\mathrm{KFL}$), an efficient reparameterization of flexible monotonic lattice regression via Kronecker product. Both computational and storage costs scale linearly in the number of input features, which is a significant improvement over existing methods that grow exponentially. We also show that we can still properly enforce monotonicity and other shape constraints. The $\mathrm{KFL}$ function class consists of products of piecewise-linear functions, and the size of the function class can be further increased through ensembling. We prove that the function class of an ensemble of $M$ base $\mathrm{KFL}$ models strictly increases as $M$ increases up to a certain threshold. Beyond this threshold, every multilinear interpolated lattice function can be expressed. Our experimental results demonstrate that $\mathrm{KFL}$ trains faster with fewer parameters while still achieving accuracy and evaluation speeds comparable to or better than the baseline methods and preserving monotonicity guarantees on the learned model. | https://openreview.net/pdf/86fd2e9cf39fac3f9c7f37e71683f60eb7e3e575.pdf |
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation | https://openreview.net/forum?id=MJAqnaC2vO1 | https://openreview.net/forum?id=MJAqnaC2vO1 | Hao Li,Chenxin Tao,Xizhou Zhu,Xiaogang Wang,Gao Huang,Jifeng Dai | ICLR 2021,Poster | Designing proper loss functions is essential in training deep networks. Especially in the field of semantic segmentation, various evaluation metrics have been proposed for diverse scenarios. Despite the success of the widely adopted cross-entropy loss and its variants, the mis-alignment between the loss functions and evaluation metrics degrades the network performance. Meanwhile, manually designing loss functions for each specific metric requires expertise and significant manpower. In this paper, we propose to automate the design of metric-specific loss functions by searching differentiable surrogate losses for each metric. We substitute the non-differentiable operations in the metrics with parameterized functions, and conduct parameter search to optimize the shape of loss surfaces. Two constraints are introduced to regularize the search space and make the search efficient. Extensive experiments on PASCAL VOC and Cityscapes demonstrate that the searched surrogate losses outperform the manually designed loss functions consistently. The searched losses can generalize well to other datasets and networks. Code shall be released at https://github.com/fundamentalvision/Auto-Seg-Loss. | https://openreview.net/pdf/c95e4fe3eeed14d6b9ea3a3421f111e68f1ba477.pdf |
Combining Physics and Machine Learning for Network Flow Estimation | https://openreview.net/forum?id=l0V53bErniB | https://openreview.net/forum?id=l0V53bErniB | Arlei Lopes da Silva,Furkan Kocayusufoglu,Saber Jafarpour,Francesco Bullo,Ananthram Swami,Ambuj Singh | ICLR 2021,Poster | The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying \textit{physics} (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, the resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. | https://openreview.net/pdf/9dc2744a465941220de07cf308acf822ec8aaa64.pdf |
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation | https://openreview.net/forum?id=pmj131uIL9H | https://openreview.net/forum?id=pmj131uIL9H | Angtian Wang,Adam Kortylewski,Alan Yuille | ICLR 2021,Poster | 3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust to partial occlusion. Inspired by the robustness of generative vision models to partial occlusion, we propose to integrate deep neural networks with 3D generative representations of objects into a unified neural architecture that we term NeMo. In particular, NeMo learns a generative model of neural feature activations at each vertex on a dense 3D mesh. Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image. To avoid local optima in the reconstruction loss, we train the feature extractor to maximize the distance between the individual feature representations on the mesh using contrastive learning. Our extensive experiments on PASCAL3D+, occluded-PASCAL3D+ and ObjectNet3D show that NeMo is much more robust to partial occlusion compared to standard deep networks, while retaining competitive performance on non-occluded data. Interestingly, our experiments also show that NeMo performs reasonably well even when the mesh representation only crudely approximates the true object geometry with a cuboid, hence revealing that the detailed 3D geometry is not needed for accurate 3D pose estimation. | https://openreview.net/pdf/8b6175a9362d0d2b28d4e33e82e7dbcef30c90a0.pdf |
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision | https://openreview.net/forum?id=Wi5KUNlqWty | https://openreview.net/forum?id=Wi5KUNlqWty | Dongkwan Kim,Alice Oh | ICLR 2021,Poster | Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an improved graph attention model for noisy graphs. Specifically, we exploit two attention forms compatible with a self-supervised task to predict edges, whose presence and absence contain the inherent information about the importance of the relationships between nodes. By encoding edges, SuperGAT learns more expressive attention in distinguishing mislinked neighbors. We find two graph characteristics influence the effectiveness of attention forms and self-supervision: homophily and average degree. Thus, our recipe provides guidance on which attention design to use when those two graph characteristics are known. Our experiment on 17 real-world datasets demonstrates that our recipe generalizes across 15 datasets of them, and our models designed by recipe show improved performance over baselines. | https://openreview.net/pdf/9a9b994ea7dc4b59415ee1780753f7061f19eea4.pdf |
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering | https://openreview.net/forum?id=gV3wdEOGy_V | https://openreview.net/forum?id=gV3wdEOGy_V | Tsung Wei Tsai,Chongxuan Li,Jun Zhu | ICLR 2021,Poster | We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating function to partition an unlabeled dataset into subsets according to the latent semantics and multiple experts to discriminate distinct subsets of instances assigned to them in a contrastive learning manner. To solve the nontrivial inference and learning problems caused by the latent variables, we further develop a scalable variant of the Expectation-Maximization (EM) algorithm for MiCE and provide proof of the convergence. Empirically, we evaluate the clustering performance of MiCE on four widely adopted natural image datasets. MiCE achieves significantly better results than various previous methods and a strong contrastive learning baseline. | https://openreview.net/pdf/44a32c550457ebb4f9a6863753563e1f3b2740db.pdf |
Anytime Sampling for Autoregressive Models via Ordered Autoencoding | https://openreview.net/forum?id=TSRTzJnuEBS | https://openreview.net/forum?id=TSRTzJnuEBS | Yilun Xu,Yang Song,Sahaj Garg,Linyuan Gong,Rui Shu,Aditya Grover,Stefano Ermon | ICLR 2021,Poster | Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling process that is sequential in nature and typically scales linearly with respect to the data dimension. To address this difficulty, we propose a new family of autoregressive models that enables anytime sampling. Inspired by Principal Component Analysis, we learn a structured representation space where dimensions are ordered based on their importance with respect to reconstruction. Using an autoregressive model in this latent space, we trade off sample quality for computational efficiency by truncating the generation process before decoding into the original data space. Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling. The approach suffers almost no loss in sample quality (measured by FID) using only 60\% to 80\% of all latent dimensions for image data. Code is available at https://github.com/Newbeeer/Anytime-Auto-Regressive-Model. | https://openreview.net/pdf/1367785ee095d8df274476d18c53a21ad0d67173.pdf |
Initialization and Regularization of Factorized Neural Layers | https://openreview.net/forum?id=KTlJT1nof6d | https://openreview.net/forum?id=KTlJT1nof6d | Mikhail Khodak,Neil A. Tenenholtz,Lester Mackey,Nicolo Fusi | ICLR 2021,Poster | Factorized layers—operations parameterized by products of two or more matrices—occur in a variety of deep learning contexts, including compressed model training, certain types of knowledge distillation, and multi-head self-attention architectures. We study how to initialize and regularize deep nets containing such layers, examining two simple, understudied schemes, spectral initialization and Frobenius decay, for improving their performance. The guiding insight is to design optimization routines for these networks that are as close as possible to that of their well-tuned, non-decomposed counterparts; we back this intuition with an analysis of how the initialization and regularization schemes impact training with gradient descent, drawing on modern attempts to understand the interplay of weight-decay and batch-normalization. Empirically, we highlight the benefits of spectral initialization and Frobenius decay across a variety of settings. In model compression, we show that they enable low-rank methods to significantly outperform both unstructured sparsity and tensor methods on the task of training low-memory residual networks; analogs of the schemes also improve the performance of tensor decomposition techniques. For knowledge distillation, Frobenius decay enables a simple, overcomplete baseline that yields a compact model from over-parameterized training without requiring retraining with or pruning a teacher network. Finally, we show how both schemes applied to multi-head attention lead to improved performance on both translation and unsupervised pre-training. | https://openreview.net/pdf/d22bc639b3e05ed1c862db4eaa41726d1f24406d.pdf |
CO2: Consistent Contrast for Unsupervised Visual Representation Learning | https://openreview.net/forum?id=U4XLJhqwNF1 | https://openreview.net/forum?id=U4XLJhqwNF1 | Chen Wei,Huiyu Wang,Wei Shen,Alan Yuille | ICLR 2021,Poster | Contrastive learning has recently been a core for unsupervised visual representation learning. Without human annotation, the common practice is to perform an instance discrimination task: Given a query image crop, label crops from the same image as positives, and crops from other randomly sampled images as negatives. An important limitation of this label assignment is that it can not reflect the heterogeneous similarity of the query crop to crops from other images, but regarding them as equally negative. To address this issue, inspired by consistency regularization in semi-supervised learning, we propose Consistent Contrast (CO2), which introduces a consistency term into unsupervised contrastive learning framework. The consistency term takes the similarity of the query crop to crops from other images as unlabeled, and the corresponding similarity of a positive crop as a pseudo label. It then encourages consistency between these two similarities. Empirically, CO2 improves Momentum Contrast (MoCo) by 2.9% top-1 accuracy on ImageNet linear protocol, 3.8% and 1.1% top-5 accuracy on 1% and 10% labeled semi-supervised settings. It also transfers to image classification, object detection, and semantic segmentation on PASCAL VOC. This shows that CO2 learns better visual representations for downstream tasks. | https://openreview.net/pdf/3bf139a485a1e2e65ce2dc0b66f592976f4cfe61.pdf |
DC3: A learning method for optimization with hard constraints | https://openreview.net/forum?id=V1ZHVxJ6dSS | https://openreview.net/forum?id=V1ZHVxJ6dSS | Priya L. Donti,David Rolnick,J Zico Kolter | ICLR 2021,Poster | Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility. | https://openreview.net/pdf/07736947eb069cd731941458cb558b9bbec98ba1.pdf |
Enforcing robust control guarantees within neural network policies | https://openreview.net/forum?id=5lhWG3Hj2By | https://openreview.net/forum?id=5lhWG3Hj2By | Priya L. Donti,Melrose Roderick,Mahyar Fazlyab,J Zico Kolter | ICLR 2021,Poster | When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance. While robust control methods provide rigorous guarantees on system stability under certain worst-case disturbances, they often yield simple controllers that perform poorly in the average (non-worst) case. In contrast, nonlinear control methods trained using deep learning have achieved state-of-the-art performance on many control tasks, but often lack robustness guarantees. In this paper, we propose a technique that combines the strengths of these two approaches: constructing a generic nonlinear control policy class, parameterized by neural networks, that nonetheless enforces the same provable robustness criteria as robust control. Specifically, our approach entails integrating custom convex-optimization-based projection layers into a neural network-based policy. We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods. | https://openreview.net/pdf/c31f3f12091e950c7b2a58373e1af5225f1b5377.pdf |
Robust and Generalizable Visual Representation Learning via Random Convolutions | https://openreview.net/forum?id=BVSM0x3EDK6 | https://openreview.net/forum?id=BVSM0x3EDK6 | Zhenlin Xu,Deyi Liu,Junlin Yang,Colin Raffel,Marc Niethammer | ICLR 2021,Poster | While successful for various computer vision tasks, deep neural networks have shown to be vulnerable to texture style shifts and small perturbations to which humans are robust. In this work, we show that the robustness of neural networks can be greatly improved through the use of random convolutions as data augmentation. Random convolutions are approximately shape-preserving and may distort local textures. Intuitively, randomized convolutions create an infinite number of new domains with similar global shapes but random local texture. Therefore, we explore using outputs of multi-scale random convolutions as new images or mixing them with the original images during training. When applying a network trained with our approach to unseen domains, our method consistently improves the performance on domain generalization benchmarks and is scalable to ImageNet. In particular, in the challenging scenario of generalizing to the sketch domain in PACS and to ImageNet-Sketch, our method outperforms state-of-art methods by a large margin. More interestingly, our method can benefit downstream tasks by providing a more robust pretrained visual representation. | https://openreview.net/pdf/10040feb356410f9e8b8ee9678ecb702e0534990.pdf |
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic | https://openreview.net/forum?id=3SqrRe8FWQ- | https://openreview.net/forum?id=3SqrRe8FWQ- | Renkun Ni,Hong-min Chu,Oscar Castaneda,Ping-yeh Chiang,Christoph Studer,Tom Goldstein | ICLR 2021,Poster | Low-precision neural networks represent both weights and activations with few bits, drastically reducing the cost of multiplications. Meanwhile, these products are accumulated using high-precision (typically 32-bit) additions. Additions dominate the arithmetic complexity of inference in quantized (e.g., binary) nets, and high precision is needed to avoid overflow. To further optimize inference, we propose WrapNet, an architecture that adapts neural networks to use low-precision (8-bit) additions while achieving classification accuracy comparable to their 32-bit counterparts. We achieve resilience to low-precision accumulation by inserting a cyclic activation layer that makes results invariant to overflow. We demonstrate the efficacy of our approach using both software and hardware platforms. | https://openreview.net/pdf/f4b38b314f5630e45d41c5ff39eace1a2b8afca6.pdf |
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments | https://openreview.net/forum?id=MtEE0CktZht | https://openreview.net/forum?id=MtEE0CktZht | Daochen Zha,Wenye Ma,Lei Yuan,Xia Hu,Ji Liu | ICLR 2021,Poster | Exploration under sparse reward is a long-standing challenge of model-free reinforcement learning. The state-of-the-art methods address this challenge by introducing intrinsic rewards to encourage exploration in novel states or uncertain environment dynamics. Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once. Motivated by how humans distinguish good exploration behaviors by looking into the entire episode, we introduce RAPID, a simple yet effective episode-level exploration method for procedurally-generated environments. RAPID regards each episode as a whole and gives an episodic exploration score from both per-episode and long-term views. Those highly scored episodes are treated as good exploration behaviors and are stored in a small ranking buffer. The agent then imitates the episodes in the buffer to reproduce the past good exploration behaviors. We demonstrate our method on several procedurally-generated MiniGrid environments, a first-person-view 3D Maze navigation task from MiniWorld, and several sparse MuJoCo tasks. The results show that RAPID significantly outperforms the state-of-the-art intrinsic reward strategies in terms of sample efficiency and final performance. The code is available at https://github.com/daochenzha/rapid | https://openreview.net/pdf/1a3486946250b679d39b93e6b12ff912023a3955.pdf |
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models | https://openreview.net/forum?id=gwFTuzxJW0 | https://openreview.net/forum?id=gwFTuzxJW0 | Mitch Hill,Jonathan Craig Mitchell,Song-Chun Zhu | ICLR 2021,Poster | The vulnerability of deep networks to adversarial attacks is a central problem for deep learning from the perspective of both cognition and security. The current most successful defense method is to train a classifier using adversarial images created during learning. Another defense approach involves transformation or purification of the original input to remove adversarial signals before the image is classified. We focus on defending naturally-trained classifiers using Markov Chain Monte Carlo (MCMC) sampling with an Energy-Based Model (EBM) for adversarial purification. In contrast to adversarial training, our approach is intended to secure highly vulnerable pre-existing classifiers. To our knowledge, no prior defensive transformation is capable of securing naturally-trained classifiers, and our method is the first to validate a post-training defense approach that is distinct from current successful defenses which modify classifier training.
The memoryless behavior of long-run MCMC sampling will eventually remove adversarial signals, while metastable behavior preserves consistent appearance of MCMC samples after many steps to allow accurate long-run prediction. Balancing these factors can lead to effective purification and robust classification. We evaluate adversarial defense with an EBM using the strongest known attacks against purification. Our contributions are 1) an improved method for training EBM's with realistic long-run MCMC samples for effective purification, 2) an Expectation-Over-Transformation (EOT) defense that resolves ambiguities for evaluating stochastic defenses and from which the EOT attack naturally follows, and 3) state-of-the-art adversarial defense for naturally-trained classifiers and competitive defense compared to adversarial training on CIFAR-10, SVHN, and CIFAR-100. Our code and pre-trained models are available at https://github.com/point0bar1/ebm-defense. | https://openreview.net/pdf/f72ab5e343005a27c47de932d2ee35163cb3c617.pdf |
Nearest Neighbor Machine Translation | https://openreview.net/forum?id=7wCBOfJ8hJM | https://openreview.net/forum?id=7wCBOfJ8hJM | Urvashi Khandelwal,Angela Fan,Dan Jurafsky,Luke Zettlemoyer,Mike Lewis | ICLR 2021,Poster | We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which predicts tokens with a nearest-neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder direct access to billions of examples at test time, resulting in a highly expressive model that consistently improves performance across many settings. Simply adding nearest-neighbor search improves a state-of-the-art German-English translation model by 1.5 BLEU. $k$NN-MT allows a single model to be adapted to diverse domains by using a domain-specific datastore, improving results by an average of 9.2 BLEU over zero-shot transfer, and achieving new state-of-the-art results---without training on these domains. A massively multilingual model can also be specialized for particular language pairs, with improvements of 3 BLEU for translating from English into German and Chinese. Qualitatively, $k$NN-MT is easily interpretable; it combines source and target context to retrieve highly relevant examples. | https://openreview.net/pdf/8c353a40fa6aa514c510ad3cc89f331af070681b.pdf |
Learning Hyperbolic Representations of Topological Features | https://openreview.net/forum?id=yqPnIRhHtZv | https://openreview.net/forum?id=yqPnIRhHtZv | Panagiotis Kyriakis,Iordanis Fostiropoulos,Paul Bogdan | ICLR 2021,Poster | Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current state of the art methods are restricted in terms of their expressivity as they are focused on Euclidean representations. Persistence diagrams often contain features of infinite persistence (i.e., essential features) and Euclidean spaces shrink their importance relative to non-essential features because they cannot assign infinite distance to finite points. To deal with this issue, we propose a method to learn representations of persistence diagrams on hyperbolic spaces, more specifically on the Poincare ball. By representing features of infinite persistence infinitesimally close to the boundary of the ball, their distance to non-essential features approaches infinity, thereby their relative importance is preserved. This is achieved without utilizing extremely high values for the learnable parameters, thus the representation can be fed into downstream optimization methods and trained efficiently in an end-to-end fashion. We present experimental results on graph and image classification tasks and show that the performance of our method is on par with or exceeds the performance of other state of the art methods.
| https://openreview.net/pdf/90d9d8bd76c09f1561cba895e6574b5a137974f8.pdf |
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs | https://openreview.net/forum?id=8nl0k08uMi | https://openreview.net/forum?id=8nl0k08uMi | Matthew L Leavitt,Ari S. Morcos | ICLR 2021,Poster | The properties of individual neurons are often analyzed in order to understand the biological and artificial neural networks in which they're embedded. Class selectivity—typically defined as how different a neuron's responses are across different classes of stimuli or data samples—is commonly used for this purpose. However, it remains an open question whether it is necessary and/or sufficient for deep neural networks (DNNs) to learn class selectivity in individual units. We investigated the causal impact of class selectivity on network function by directly regularizing for or against class selectivity. Using this regularizer to reduce class selectivity across units in convolutional neural networks increased test accuracy by over 2% in ResNet18 and 1% in ResNet50 trained on Tiny ImageNet. For ResNet20 trained on CIFAR10 we could reduce class selectivity by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (~2%) drop in test accuracy. In contrast, regularizing to increase class selectivity significantly decreased test accuracy across all models and datasets. These results indicate that class selectivity in individual units is neither sufficient nor strictly necessary, and can even impair DNN performance. They also encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function. | https://openreview.net/pdf/17afa91e20d55de3a216e07f59e5ab74d27027a6.pdf |
Integrating Categorical Semantics into Unsupervised Domain Translation | https://openreview.net/forum?id=IMPA6MndSXU | https://openreview.net/forum?id=IMPA6MndSXU | Samuel Lavoie-Marchildon,Faruk Ahmed,Aaron Courville | ICLR 2021,Poster | While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical semantic features could broaden its applicability. In particular, we demonstrate that categorical semantics improves the translation between perceptually different domains sharing multiple object categories. We propose a method to learn, in an unsupervised manner, categorical semantic features (such as object labels) that are invariant of the source and target domains. We show that conditioning the style encoder of unsupervised domain translation methods on the learned categorical semantics leads to a translation preserving the digits on MNIST$\leftrightarrow$SVHN and to a more realistic stylization on Sketches$\to$Reals. | https://openreview.net/pdf/63644971703ef2d045a2404b8db5e211dd90c275.pdf |
Zero-shot Synthesis with Group-Supervised Learning | https://openreview.net/forum?id=8wqCDnBmnrT | https://openreview.net/forum?id=8wqCDnBmnrT | Yunhao Ge,Sami Abu-El-Haija,Gan Xin,Laurent Itti | ICLR 2021,Poster | Visual cognition of primates is superior to that of artificial neural networks in its ability to “envision” a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc. To aid neural networks to envision objects with different attributes, we propose a family of objective functions, expressed on groups of examples, as a novel learning framework that we term Group-Supervised Learning (GSL). GSL allows us to decompose inputs into a disentangled representation with swappable components, that can be recombined to synthesize new samples. For instance, images of red boats & blue cars can be decomposed and recombined to synthesize novel images of red cars. We propose an implementation based on auto-encoder, termed group-supervised zero-shot synthesis network (GZS-Net) trained with our learning framework, that can produce a high-quality red car even if no such example is witnessed during training. We test our model and learning framework on existing benchmarks, in addition to a new dataset that we open-source. We qualitatively and quantitatively demonstrate that GZS-Net trained with GSL outperforms state-of-the-art methods | https://openreview.net/pdf/5cc8c0d37d4aa9db1645e1181b60428d215c25b4.pdf |
VA-RED$^2$: Video Adaptive Redundancy Reduction | https://openreview.net/forum?id=g21u6nlbPzn | https://openreview.net/forum?id=g21u6nlbPzn | Bowen Pan,Rameswar Panda,Camilo Luciano Fosco,Chung-Ching Lin,Alex J Andonian,Yue Meng,Kate Saenko,Aude Oliva,Rogerio Feris | ICLR 2021,Poster | Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both. The type of redundant features depends on the dynamics and type of events in the video: static videos have more temporal redundancy while videos focusing on objects tend to have more channel redundancy. Here we present a redundancy reduction framework, termed VA-RED$^2$, which is input-dependent. Specifically, our VA-RED$^2$ framework uses an input-dependent policy to decide how many features need to be computed for temporal and channel dimensions. To keep the capacity of the original model, after fully computing the necessary features, we reconstruct the remaining redundant features from those using cheap linear operations. We learn the adaptive policy jointly with the network weights in a differentiable way with a shared-weight mechanism, making it highly efficient. Extensive experiments on multiple video datasets and different visual tasks show that our framework achieves $20\% - 40\%$ reduction in computation (FLOPs) when compared to state-of-the-art methods without any performance loss. Project page: http://people.csail.mit.edu/bpan/va-red/. | https://openreview.net/pdf/474acfaf4c205394a38367bb1c69399a31b1264e.pdf |
BERTology Meets Biology: Interpreting Attention in Protein Language Models | https://openreview.net/forum?id=YWtLZvLmud7 | https://openreview.net/forum?id=YWtLZvLmud7 | Jesse Vig,Ali Madani,Lav R. Varshney,Caiming Xiong,richard socher,Nazneen Rajani | ICLR 2021,Poster | Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. In this work, we demonstrate a set of methods for analyzing protein Transformer models through the lens of attention. We show that attention: (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We find this behavior to be consistent across three Transformer architectures (BERT, ALBERT, XLNet) and two distinct protein datasets. We also present a three-dimensional visualization of the interaction between attention and protein structure. Code for visualization and analysis is available at https://github.com/salesforce/provis. | https://openreview.net/pdf/8fe71cea00c77fd4bce646070f7b96ef9300e91d.pdf |
Trajectory Prediction using Equivariant Continuous Convolution | https://openreview.net/forum?id=J8_GttYLFgr | https://openreview.net/forum?id=J8_GttYLFgr | Robin Walters,Jinxi Li,Rose Yu | ICLR 2021,Poster | Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetry in real-world trajectories. We propose a novel model, Equivariant Continous COnvolution (ECCO) for improved trajectory prediction. ECCO uses rotationally-equivariant continuous convolutions to embed the symmetries of the system. On both vehicle and pedestrian trajectory datasets, ECCO attains competitive accuracy with significantly fewer parameters. It is also more sample efficient, generalizing automatically from few data points in any orientation. Lastly, ECCO improves generalization with equivariance, resulting in more physically consistent predictions. Our method provides a fresh perspective towards increasing trust and transparency in deep learning models. Our code and data can be found at https://github.com/Rose-STL-Lab/ECCO. | https://openreview.net/pdf/e66067f5a20544270d6679390629804b037a33d7.pdf |
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models | https://openreview.net/forum?id=XOjv2HxIF6i | https://openreview.net/forum?id=XOjv2HxIF6i | Siavash Khodadadeh,Sharare Zehtabian,Saeed Vahidian,Weijia Wang,Bill Lin,Ladislau Boloni | ICLR 2021,Poster | Several recently proposed unsupervised meta-learning approaches rely on synthetic meta-tasks created using techniques such as random selection, clustering and/or augmentation. In this work, we describe a novel approach that generates meta-tasks using generative models. The proposed family of algorithms generate pairs of in-class and out-of-class samples from the latent space in a principled way, allowing us to create synthetic classes forming the training and validation data of a meta-task. We find that the proposed approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM), outperforms or is competitive with current unsupervised learning baselines on few-shot classification tasks on the most widely used benchmark datasets. | https://openreview.net/pdf/8242bb1cb5c7dc59b17a736254778eca82c71c06.pdf |
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization | https://openreview.net/forum?id=mEdwVCRJuX4 | https://openreview.net/forum?id=mEdwVCRJuX4 | Kaidi Cao,Yining Chen,Junwei Lu,Nikos Arechiga,Adrien Gaidon,Tengyu Ma | ICLR 2021,Poster | Real-world large-scale datasets are heteroskedastic and imbalanced --- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning. | https://openreview.net/pdf/99d4ce0622fd824cd067efb8dda5611146eb4070.pdf |
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning | https://openreview.net/forum?id=O9bnihsFfXU | https://openreview.net/forum?id=O9bnihsFfXU | Aviral Kumar,Rishabh Agarwal,Dibya Ghosh,Sergey Levine | ICLR 2021,Poster | We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are trained with gradient descent using iterated regression onto target values generated by previous instances of the value network, more gradient updates decrease the expressivity of the current value network. We char- acterize this loss of expressivity via a drop in the rank of the learned value net- work features, and show that this typically corresponds to a performance drop. We demonstrate this phenomenon on Atari and Gym benchmarks, in both offline and online RL settings. We formally analyze this phenomenon and show that it results from a pathological interaction between bootstrapping and gradient-based optimization. We further show that mitigating implicit under-parameterization by controlling rank collapse can improve performance. | https://openreview.net/pdf/766d703650bcb86c35c53681b89ad0a2aba30504.pdf |
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis | https://openreview.net/forum?id=ESG-DMKQKsD | https://openreview.net/forum?id=ESG-DMKQKsD | Zhipeng Bao,Yu-Xiong Wang,Martial Hebert | ICLR 2021,Poster | We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. While existing work copes with two or more tasks mainly by multi-task learning of shareable feature representations, we take a different perspective. We focus on the interaction and cooperation between a generative model and a discriminative model, in a way that facilitates knowledge to flow across tasks in complementary directions. To this end, we propose bowtie networks that jointly learn 3D geometric and semantic representations with a feedback loop. Experimental evaluation on challenging fine-grained recognition datasets demonstrates that our synthesized images are realistic from multiple viewpoints and significantly improve recognition performance as ways of data augmentation, especially in the low-data regime. | https://openreview.net/pdf/7991edacbb1e82b6f57da8010cb4bbb939dd5461.pdf |
Saliency is a Possible Red Herring When Diagnosing Poor Generalization | https://openreview.net/forum?id=c9-WeM-ceB | https://openreview.net/forum?id=c9-WeM-ceB | Joseph D Viviano,Becks Simpson,Francis Dutil,Yoshua Bengio,Joseph Paul Cohen | ICLR 2021,Poster | Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert have labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as 'attribution priors' as well as saliency maps for explainable predictions. | https://openreview.net/pdf/11d962b7010ee77620458bfc746f11370a23e2dc.pdf |
What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions | https://openreview.net/forum?id=Qm8UNVCFdh | https://openreview.net/forum?id=Qm8UNVCFdh | Kiana Ehsani,Daniel Gordon,Thomas Hai Dang Nguyen,Roozbeh Mottaghi,Ali Farhadi | ICLR 2021,Poster | Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. For this study, we collect a dataset of human interactions capturing body part movements and gaze in their daily lives. Our experiments show that our ``"muscly-supervised" representation that encodes interaction and attention cues outperforms a visual-only state-of-the-art method MoCo (He et al.,2020), on a variety of target tasks: scene classification (semantic), action recognition (temporal), depth estimation (geometric), dynamics prediction (physics) and walkable surface estimation (affordance). Our code and dataset are available at: https://github.com/ehsanik/muscleTorch. | https://openreview.net/pdf/68e63865a158882a87332ea2d2b8f196693b3f62.pdf |
Conservative Safety Critics for Exploration | https://openreview.net/forum?id=iaO86DUuKi | https://openreview.net/forum?id=iaO86DUuKi | Homanga Bharadhwaj,Aviral Kumar,Nicholas Rhinehart,Sergey Levine,Florian Shkurti,Animesh Garg | ICLR 2021,Poster | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL, by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are satisfied with high probability during training, derive provable convergence guarantees for our approach which is no worse asymptotically then standard RL, and empirically demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Our results demonstrate that the proposed approach can achieve competitive task performance, while incurring significantly lower catastrophic failure rates during training as compared to prior methods. Videos are at this URL https://sites.google.com/view/conservative-safety-critics/ | https://openreview.net/pdf/31cfa17ce6b5a4dd1c7e3bf3ce4c025642d3199e.pdf |
Multi-Time Attention Networks for Irregularly Sampled Time Series | https://openreview.net/forum?id=4c0J6lwQ4_ | https://openreview.net/forum?id=4c0J6lwQ4_ | Satya Narayan Shukla,Benjamin Marlin | ICLR 2021,Poster | Irregular sampling occurs in many time series modeling applications where it presents a significant challenge to standard deep learning models. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. In this paper, we propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Multi-Time Attention Networks learn an embedding of continuous time values and use an attention mechanism to produce a fixed-length representation of a time series containing a variable number of observations. We investigate the performance of this framework on interpolation and classification tasks using multiple datasets. Our results show that the proposed approach performs as well or better than a range of baseline and recently proposed models while offering significantly faster training times than current state-of-the-art methods. | https://openreview.net/pdf/35e8d4bd3fd59a402e488637f16e0fec9e36706a.pdf |
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning | https://openreview.net/forum?id=6DOZ8XNNfGN | https://openreview.net/forum?id=6DOZ8XNNfGN | Elan Sopher Markowitz,Keshav Balasubramanian,Mehrnoosh Mirtaheri,Sami Abu-El-Haija,Bryan Perozzi,Greg Ver Steeg,Aram Galstyan | ICLR 2021,Poster | Graph Representation Learning (GRL) methods have impacted fields from chemistry to social science. However, their algorithmic implementations are specialized to specific use-cases e.g. "message passing" methods are run differently from "node embedding" ones. Despite their apparent differences, all these methods utilize the graph structure, and therefore, their learning can be approximated with stochastic graph traversals. We propose Graph Traversal via Tensor Functionals (GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs. GTTF is founded upon a data structure (stored as a sparse tensor) and a stochastic graph traversal algorithm (described using tensor operations). The algorithm is a functional that accept two functions, and can be specialized to obtain a variety of GRL models and objectives, simply by changing those two functions. We show for a wide class of methods, our algorithm learns in an unbiased fashion and, in expectation, approximates the learning as if the specialized implementations were run directly.
With these capabilities, we scale otherwise non-scalable methods to set state-of-the-art on large graph datasets while being more efficient than existing GRL libraries -- with only a handful of lines of code for each method specialization. | https://openreview.net/pdf/32f7d4bfb27e1afd27046d2e43aa02a64afd5952.pdf |
What they do when in doubt: a study of inductive biases in seq2seq learners | https://openreview.net/forum?id=YmA86Zo-P_t | https://openreview.net/forum?id=YmA86Zo-P_t | Eugene Kharitonov,Rahma Chaabouni | ICLR 2021,Poster | Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize. We address that by investigating how popular seq2seq learners generalize in tasks that have high ambiguity in the training data. We use four new tasks to study learners' preferences for memorization, arithmetic, hierarchical, and compositional reasoning. Further, we connect to Solomonoff's theory of induction and propose to use description length as a principled and sensitive measure of inductive biases. In our experimental study, we find that LSTM-based learners can learn to perform counting, addition, and multiplication by a constant from a single training example. Furthermore, Transformer and LSTM-based learners show a bias toward the hierarchical induction over the linear one, while CNN-based learners prefer the opposite. The latter also show a bias toward a compositional generalization over memorization. Finally, across all our experiments, description length proved to be a sensitive measure of inductive biases. | https://openreview.net/pdf/585c196f903498736cc63f2856f42b9c3fd4139d.pdf |
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data | https://openreview.net/forum?id=YtMG5ex0ou | https://openreview.net/forum?id=YtMG5ex0ou | Francesco Tonolini,Pablo Garcia Moreno,Andreas Damianou,Roderick Murray-Smith | ICLR 2021,Poster | We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this set-ting, direct application of classical variational methods often gives rise to collapsed densities that do not adequately explore the solution space. Instead, we derive our novel reduced entropy condition approximate inference method that results in rich posteriors. We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model. | https://openreview.net/pdf/1a034aa157281f3316e3bb5608ae9c2bb7e13e30.pdf |
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning | https://openreview.net/forum?id=V69LGwJ0lIN | https://openreview.net/forum?id=V69LGwJ0lIN | Anurag Ajay,Aviral Kumar,Pulkit Agrawal,Sergey Levine,Ofir Nachum | ICLR 2021,Poster | Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent’s ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. In this work, we focus on this offline setting. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors before using these primitives for downstream task learning. Primitives extracted in this way serve two purposes: they delineate the behaviors that are supported by the data from those that are not, making them useful for avoiding distributional shift in offline RL; and they provide a degree of temporal abstraction, which reduces the effective horizon yielding better learning in theory, and improved offline RL in practice. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning as well as exploration and transfer in online RL on a variety of benchmark domains. Visualizations and code are available at https://sites.google.com/view/opal-iclr | https://openreview.net/pdf/b57bd58e05e17b49da003dded33a75506b9ddbac.pdf |
Knowledge distillation via softmax regression representation learning | https://openreview.net/forum?id=ZzwDy_wiWv | https://openreview.net/forum?id=ZzwDy_wiWv | Jing Yang,Brais Martinez,Adrian Bulat,Georgios Tzimiropoulos | ICLR 2021,Poster | This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning. Previous distillation methods which typically impose direct feature matching between the student and the teacher do not take into account the classification problem at hand. On the contrary, our distillation method decouples representation learning and classification and utilizes the teacher's pre-trained classifier to train the student's penultimate layer feature. In particular, for the same input image, we wish the teacher's and student's feature to produce the same output when passed through the teacher's classifier which is achieved with a simple $L_2$ loss. Our method is extremely simple to implement and straightforward to train and is shown to consistently outperform previous state-of-the-art methods over a large set of experimental settings including different (a) network architectures, (b) teacher-student capacities, (c) datasets, and (d) domains. The code will be available at \url{https://github.com/jingyang2017/KD_SRRL}. | https://openreview.net/pdf/94c700e0e7f29d564e416cc3b56b6b350c29aafb.pdf |
Large Associative Memory Problem in Neurobiology and Machine Learning | https://openreview.net/forum?id=X4y_10OX-hX | https://openreview.net/forum?id=X4y_10OX-hX | Dmitry Krotov,John J. Hopfield | ICLR 2021,Poster | Dense Associative Memories or modern Hopfield networks permit storage and reliable retrieval of an exponentially large (in the dimension of feature space) number of memories. At the same time, their naive implementation is non-biological, since it seemingly requires the existence of many-body synaptic junctions between the neurons. We show that these models are effective descriptions of a more microscopic (written in terms of biological degrees of freedom) theory that has additional (hidden) neurons and only requires two-body interactions between them. For this reason our proposed microscopic theory is a valid model of large associative memory with a degree of biological plausibility. The dynamics of our network and its reduced dimensional equivalent both minimize energy (Lyapunov) functions. When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, e.g. the model presented in "Hopfield Networks is All You Need" paper. We also provide an alternative derivation of the energy function and the update rule proposed in the aforementioned paper and clarify the relationships between various models of this class. | https://openreview.net/pdf/707e3c461fd1c2e94f05cad3278e6aaa3a32f2de.pdf |
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting | https://openreview.net/forum?id=tHgJoMfy6nI | https://openreview.net/forum?id=tHgJoMfy6nI | Sayna Ebrahimi,Suzanne Petryk,Akash Gokul,William Gan,Joseph E. Gonzalez,Marcus Rohrbach,trevor darrell | ICLR 2021,Poster | The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the \textit{evidence} for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has ``the right reasons'' for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}. | https://openreview.net/pdf/c9a83cab23e5aa3aa692ee2b8ff385ac2cd2da0b.pdf |
Deep Networks and the Multiple Manifold Problem | https://openreview.net/forum?id=O-6Pm_d_Q- | https://openreview.net/forum?id=O-6Pm_d_Q- | Sam Buchanan,Dar Gilboa,John Wright | ICLR 2021,Poster | We study the multiple manifold problem, a binary classification task modeled on applications in machine vision, in which a deep fully-connected neural network is trained to separate two low-dimensional submanifolds of the unit sphere. We provide an analysis of the one-dimensional case, proving for a simple manifold configuration that when the network depth $L$ is large relative to certain geometric and statistical properties of the data, the network width $n$ grows as a sufficiently large polynomial in $L$, and the number of i.i.d. samples from the manifolds is polynomial in $L$, randomly-initialized gradient descent rapidly learns to classify the two manifolds perfectly with high probability. Our analysis demonstrates concrete benefits of depth and width in the context of a practically-motivated model problem: the depth acts as a fitting resource, with larger depths corresponding to smoother networks that can more readily separate the class manifolds, and the width acts as a statistical resource, enabling concentration of the randomly-initialized network and its gradients. The argument centers around the "neural tangent kernel" of Jacot et al. and its role in the nonasymptotic analysis of training overparameterized neural networks; to this literature, we contribute essentially optimal rates of concentration for the neural tangent kernel of deep fully-connected ReLU networks, requiring width $n \geq L\,\mathrm{poly}(d_0)$ to achieve uniform concentration of the initial kernel over a $d_0$-dimensional submanifold of the unit sphere $\mathbb{S}^{n_0-1}$, and a nonasymptotic framework for establishing generalization of networks trained in the "NTK regime" with structured data. The proof makes heavy use of martingale concentration to optimally treat statistical dependencies across layers of the initial random network. This approach should be of use in establishing similar results for other network architectures. | https://openreview.net/pdf/56f1d814ac75024a949f12d01b0c52e5a0e108f3.pdf |
Interpreting Knowledge Graph Relation Representation from Word Embeddings | https://openreview.net/forum?id=gLWj29369lW | https://openreview.net/forum?id=gLWj29369lW | Carl Allen,Ivana Balazevic,Timothy Hospedales | ICLR 2021,Poster | Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-specific mapping. Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained. Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations. We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justified by our analysis. | https://openreview.net/pdf/1feb4e4d391a73dd7a1de57536d4621967f6cd43.pdf |
Learning perturbation sets for robust machine learning | https://openreview.net/forum?id=MIDckA56aD | https://openreview.net/forum?id=MIDckA56aD | Eric Wong,J Zico Kolter | ICLR 2021,Poster | Although much progress has been made towards robust deep learning, a significant gap in robustness remains between real-world perturbations and more narrowly defined sets typically studied in adversarial defenses. In this paper, we aim to bridge this gap by learning perturbation sets from data, in order to characterize real-world effects for robust training and evaluation. Specifically, we use a conditional generator that defines the perturbation set over a constrained region of the latent space. We formulate desirable properties that measure the quality of a learned perturbation set, and theoretically prove that a conditional variational autoencoder naturally satisfies these criteria. Using this framework, our approach can generate a variety of perturbations at different complexities and scales, ranging from baseline spatial transformations, through common image corruptions, to lighting variations. We measure the quality of our learned perturbation sets both quantitatively and qualitatively, finding that our models are capable of producing a diverse set of meaningful perturbations beyond the limited data seen during training. Finally, we leverage our learned perturbation sets to train models which are empirically and certifiably robust to adversarial image corruptions and adversarial lighting variations, while improving generalization on non-adversarial data. All code and configuration files for reproducing the experiments as well as pretrained model weights can be found at https://github.com/locuslab/perturbation_learning. | https://openreview.net/pdf/b2d7a9e83f89aa52841bff6e66a954313729ecb3.pdf |
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention | https://openreview.net/forum?id=ZK6vTvb84s | https://openreview.net/forum?id=ZK6vTvb84s | Grégoire Mialon,Dexiong Chen,Alexandre d'Aspremont,Julien Mairal | ICLR 2021,Poster | We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data. To address this challenging task, we introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost. Our aggregation technique admits two useful interpretations: it may be seen as a mechanism related to attention layers in neural networks, or it may be seen as a scalable surrogate of a classical optimal transport-based kernel. We experimentally demonstrate the effectiveness of our approach on biological sequences, achieving state-of-the-art results for protein fold recognition and detection of chromatin profiles tasks, and, as a proof of concept, we show promising results for processing natural language sequences. We provide an open-source implementation of our embedding that can be used alone or as a module in larger learning models at https://github.com/claying/OTK. | https://openreview.net/pdf/209f1d71b4e8e73a59634e771ff1c3a43d72b849.pdf |
RODE: Learning Roles to Decompose Multi-Agent Tasks | https://openreview.net/forum?id=TTUVg6vkNjK | https://openreview.net/forum?id=TTUVg6vkNjK | Tonghan Wang,Tarun Gupta,Anuj Mahajan,Bei Peng,Shimon Whiteson,Chongjie Zhang | ICLR 2021,Poster | Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discover such a set of roles. To solve this problem, we propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents. Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy: the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces. We further integrate information about action effects into the role policies to boost learning efficiency and policy generalization. By virtue of these advances, our method (1) outperforms the current state-of-the-art MARL algorithms on 9 of the 14 scenarios that comprise the challenging StarCraft II micromanagement benchmark and (2) achieves rapid transfer to new environments with three times the number of agents. Demonstrative videos can be viewed at https://sites.google.com/view/rode-marl. | https://openreview.net/pdf/b3099a13254cda0cd68fedf29a0227d9684bc973.pdf |
Scalable Bayesian Inverse Reinforcement Learning | https://openreview.net/forum?id=4qR3coiNaIv | https://openreview.net/forum?id=4qR3coiNaIv | Alex James Chan,Mihaela van der Schaar | ICLR 2021,Poster | Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves scale often require extensive interaction with the environment to perform well, being inappropriate for high stakes or costly applications such as healthcare. In this paper we introduce our method, Approximate Variational Reward Imitation Learning (AVRIL), that addresses both of these issues by jointly learning an approximate posterior distribution over the reward that scales to arbitrarily complicated state spaces alongside an appropriate policy in a completely offline manner through a variational approach to said latent reward. Applying our method to real medical data alongside classic control simulations, we demonstrate Bayesian reward inference in environments beyond the scope of current methods, as well as task performance competitive with focused offline imitation learning algorithms. | https://openreview.net/pdf/a94f190029f9ebd5affafc141a770843d501a8a2.pdf |
Learning "What-if" Explanations for Sequential Decision-Making | https://openreview.net/forum?id=h0de3QWtGG | https://openreview.net/forum?id=h0de3QWtGG | Ioana Bica,Daniel Jarrett,Alihan Hüyük,Mihaela van der Schaar | ICLR 2021,Poster | Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior--i.e. trajectories of observations and actions made by an expert maximizing some unknown reward function--is essential for introspecting and auditing policies in different institutions. In this paper, we propose learning explanations of expert decisions by modeling their reward function in terms of preferences with respect to ``"what if'' outcomes: Given the current history of observations, what would happen if we took a particular action? To learn these cost-benefit tradeoffs associated with the expert's actions, we integrate counterfactual reasoning into batch inverse reinforcement learning. This offers a principled way of defining reward functions and explaining expert behavior, and also satisfies the constraints of real-world decision-making---where active experimentation is often impossible (e.g. in healthcare). Additionally, by estimating the effects of different actions, counterfactuals readily tackle the off-policy nature of policy evaluation in the batch setting, and can naturally accommodate settings where the expert policies depend on histories of observations rather than just current states. Through illustrative experiments in both real and simulated medical environments, we highlight the effectiveness of our batch, counterfactual inverse reinforcement learning approach in recovering accurate and interpretable descriptions of behavior. | https://openreview.net/pdf/8007445a08db8481ba43eaac1f01ad2c9495810b.pdf |
Learning advanced mathematical computations from examples | https://openreview.net/forum?id=-gfhS00XfKj | https://openreview.net/forum?id=-gfhS00XfKj | Francois Charton,Amaury Hayat,Guillaume Lample | ICLR 2021,Poster | Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge. | https://openreview.net/pdf/fc6de8bba436a4a56d0914d23d125bfb2eb84682.pdf |
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning | https://openreview.net/forum?id=qkLMTphG5-h | https://openreview.net/forum?id=qkLMTphG5-h | Namyeong Kwon,Hwidong Na,Gabriel Huang,Simon Lacoste-Julien | ICLR 2021,Poster | Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set. In practice, training on the meta-training set may not always be an option due to data privacy concerns, intellectual property issues, or merely lack of computing resources. In this paper, we consider the novel problem of repurposing pretrained MAML checkpoints to solve new few-shot classification tasks. Because of the potential distribution mismatch, the original MAML steps may no longer be optimal. Therefore we propose an alternative meta-testing procedure and combine MAML gradient steps with adversarial training and uncertainty-based stepsize adaptation. Our method outperforms "vanilla" MAML on same-domain and cross-domains benchmarks using both SGD and Adam optimizers and shows improved robustness to the choice of base stepsize. | https://openreview.net/pdf/d7cdb4aa01e48fb3d0a82cabe99b8fe0b1c57f47.pdf |
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition | https://openreview.net/forum?id=5jRVa89sZk | https://openreview.net/forum?id=5jRVa89sZk | Yangming Li,lemao liu,Shuming Shi | ICLR 2021,Poster | In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of performance degradation. One is the reduction of annotated entities and the other is treating unlabeled entities as negative instances. The first cause has less impact than the second one and can be mitigated by adopting pretraining language models. The second cause seriously misguides a model in training and greatly affects its performances. Based on the above observations, we propose a general approach, which can almost eliminate the misguidance brought by unlabeled entities. The key idea is to use negative sampling that, to a large extent, avoids training NER models with unlabeled entities. Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines. On well-annotated datasets, our model is competitive with the state-of-the-art method. | https://openreview.net/pdf/3caa712b0d1c2caaf7b3578b53b9f9c46e78db74.pdf |
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