Datasets:

DeepNLP
/

ICLR-2021-Accepted-Papers

title stringlengths 19 143	url stringlengths 41 43	detail_url stringlengths 41 43	authors stringlengths 9 347	tags stringclasses 3 values	abstract stringlengths 457 2.38k	pdf stringlengths 71 71
On the mapping between Hopfield networks and Restricted Boltzmann Machines	https://openreview.net/forum?id=RGJbergVIoO	https://openreview.net/forum?id=RGJbergVIoO	Matthew Smart,Anton Zilman	ICLR 2021,Oral	Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact m...	https://openreview.net/pdf/3a9204f4495810f86acf886d14ee022a31d7b863.pdf
Complex Query Answering with Neural Link Predictors	https://openreview.net/forum?id=Mos9F9kDwkz	https://openreview.net/forum?id=Mos9F9kDwkz	Erik Arakelyan,Daniel Daza,Pasquale Minervini,Michael Cochez	ICLR 2021,Oral	Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions ($\land$), disjunctions ($\lor$) and existent...	https://openreview.net/pdf/f3977c5e4b8a00127c9aed2b62ae904f29f06744.pdf
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation	https://openreview.net/forum?id=Wj4ODo0uyCF	https://openreview.net/forum?id=Wj4ODo0uyCF	Biao Zhang,Ankur Bapna,Rico Sennrich,Orhan Firat	ICLR 2021,Oral	Using a mix of shared and language-specific (LS) parameters has shown promise in multilingual neural machine translation (MNMT), but the question of when and where LS capacity matters most is still under-studied. We offer such a study by proposing conditional language-specific routing (CLSR). CLSR employs hard binary ...	https://openreview.net/pdf/daf5088c43f0425f9ab145f2bb0b1db43092147f.pdf
End-to-end Adversarial Text-to-Speech	https://openreview.net/forum?id=rsf1z-JSj87	https://openreview.net/forum?id=rsf1z-JSj87	Jeff Donahue,Sander Dieleman,Mikolaj Binkowski,Erich Elsen,Karen Simonyan	ICLR 2021,Oral	Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest. In this work, we take on the challenging task of learning to synthesise speech from normalised text or phonemes in an end-to-end manner, resulting in models which oper...	https://openreview.net/pdf/37c14e59635044278b29d57254fbd09950d3d37a.pdf
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity	https://openreview.net/forum?id=gvxJzw8kW4b	https://openreview.net/forum?id=gvxJzw8kW4b	JangHyun Kim,Wonho Choo,Hosan Jeong,Hyun Oh Song	ICLR 2021,Oral	While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge. Although a number of mixup based augmentation strategies have been ...	https://openreview.net/pdf/199727fa0bf10af61eddaed5e0c98ee9bbcfdea3.pdf
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions	https://openreview.net/forum?id=Ud3DSz72nYR	https://openreview.net/forum?id=Ud3DSz72nYR	Zhengxian Lin,Kin-Ho Lam,Alan Fern	ICLR 2021,Oral	We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ES...	https://openreview.net/pdf/0b44de227203c9a6da82618d99fd47af97f88da6.pdf
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?	https://openreview.net/forum?id=uCY5MuAxcxU	https://openreview.net/forum?id=uCY5MuAxcxU	Zhiyuan Li,Yi Zhang,Sanjeev Arora	ICLR 2021,Oral	Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of \textquotedblleft better inductive bias.\textquotedblright\ However, this has not been made mathematically rigorous, and the hurdle is t...	https://openreview.net/pdf/349f4553ada8090561541dc0bcef8232a03b04ff.pdf
Iterated learning for emergent systematicity in VQA	https://openreview.net/forum?id=Pd_oMxH8IlF	https://openreview.net/forum?id=Pd_oMxH8IlF	Ankit Vani,Max Schwarzer,Yuchen Lu,Eeshan Dhekane,Aaron Courville	ICLR 2021,Oral	Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of la...	https://openreview.net/pdf/62bee9dfb73bae4271c7f80e9d64eda7effacc43.pdf
When Do Curricula Work?	https://openreview.net/forum?id=tW4QEInpni	https://openreview.net/forum?id=tW4QEInpni	Xiaoxia Wu,Ethan Dyer,Behnam Neyshabur	ICLR 2021,Oral	Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standa...	https://openreview.net/pdf/a6f2f483d8e768e936c0ab7b9c6f8209e4fb79a4.pdf
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients	https://openreview.net/forum?id=m5Qsh0kBQG	https://openreview.net/forum?id=m5Qsh0kBQG	Brenden K Petersen,Mikel Landajuela Larma,Terrell N. Mundhenk,Claudio Prata Santiago,Soo Kyung Kim,Joanne Taery Kim	ICLR 2021,Oral	Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. ...	https://openreview.net/pdf/317665469793748a5dbbedaa91f4f31e395d23bf.pdf
Improved Autoregressive Modeling with Distribution Smoothing	https://openreview.net/forum?id=rJA5Pz7lHKb	https://openreview.net/forum?id=rJA5Pz7lHKb	Chenlin Meng,Jiaming Song,Yang Song,Shengjia Zhao,Stefano Ermon	ICLR 2021,Oral	While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the model, resembling the case of adversarial examples. Inspired by a successful adversarial defense method, we incorporate randomized smoothing...	https://openreview.net/pdf/e633e1ad3eac717e6d9e931ddd05ee2e3c9031f3.pdf
Score-Based Generative Modeling through Stochastic Differential Equations	https://openreview.net/forum?id=PxTIG12RRHS	https://openreview.net/forum?id=PxTIG12RRHS	Yang Song,Jascha Sohl-Dickstein,Diederik P Kingma,Abhishek Kumar,Stefano Ermon,Ben Poole	ICLR 2021,Oral	Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution...	https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf
Global Convergence of Three-layer Neural Networks in the Mean Field Regime	https://openreview.net/forum?id=KvyxFqZS_D	https://openreview.net/forum?id=KvyxFqZS_D	Huy Tuan Pham,Phan-Minh Nguyen	ICLR 2021,Oral	In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have...	https://openreview.net/pdf/459bd57a5e53f59b4f2736fed403a773b5494f55.pdf
Rethinking Architecture Selection in Differentiable NAS	https://openreview.net/forum?id=PKubaeJkw3	https://openreview.net/forum?id=PKubaeJkw3	Ruochen Wang,Minhao Cheng,Xiangning Chen,Xiaocheng Tang,Cho-Jui Hsieh	ICLR 2021,Oral	Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search pha...	https://openreview.net/pdf/aaf87608f6fe51a76f04c614f46d177673b5751c.pdf
Evolving Reinforcement Learning Algorithms	https://openreview.net/forum?id=0XXpJ4OtjW	https://openreview.net/forum?id=0XXpJ4OtjW	John D Co-Reyes,Yingjie Miao,Daiyi Peng,Esteban Real,Quoc V Le,Sergey Levine,Honglak Lee,Aleksandra Faust	ICLR 2021,Oral	We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our m...	https://openreview.net/pdf/78e8fae1b2cfbbae3e7010ca2f27649cb057ae84.pdf
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering	https://openreview.net/forum?id=yWkP7JuHX1	https://openreview.net/forum?id=yWkP7JuHX1	Yuxuan Zhang,Wenzheng Chen,Huan Ling,Jun Gao,Yinan Zhang,Antonio Torralba,Sanja Fidler	ICLR 2021,Oral	Differentiable rendering has paved the way to training neural networks to perform “inverse graphics” tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice. Recent Generative...	https://openreview.net/pdf/ddea76d45b925517b5cf900df64b91dc3d44d918.pdf
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training	https://openreview.net/forum?id=wWK7yXkULyh	https://openreview.net/forum?id=wWK7yXkULyh	Beidi Chen,Zichang Liu,Binghui Peng,Zhaozhuo Xu,Jonathan Lingjie Li,Tri Dao,Zhao Song,Anshumali Shrivastava,Christopher Re	ICLR 2021,Oral	Recent advances by practitioners in the deep learning community have breathed new life into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in neural network (NN) training. However, while LSH has sub-linear guarantees for approximate near-neighbor search in theory, it is known to have i...	https://openreview.net/pdf/fe8456585f1f710b957e2300306b8007044772f0.pdf
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning	https://openreview.net/forum?id=Ysuv-WOFeKR	https://openreview.net/forum?id=Ysuv-WOFeKR	Avi Singh,Huihan Liu,Gaoyue Zhou,Albert Yu,Nicholas Rhinehart,Sergey Levine	ICLR 2021,Oral	Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datas...	https://openreview.net/pdf/7ace651a0c4f40194198a9ab3ea9aefadb191c77.pdf
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness	https://openreview.net/forum?id=DktZb97_Fx	https://openreview.net/forum?id=DktZb97_Fx	Mikhail Yurochkin,Yuekai Sun	ICLR 2021,Oral	In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regu...	https://openreview.net/pdf/80d776638f6b356e13bef121dae894df08d5545f.pdf
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data	https://openreview.net/forum?id=rC8sJ4i6kaH	https://openreview.net/forum?id=rC8sJ4i6kaH	Colin Wei,Kendrick Shen,Yining Chen,Tengyu Ma	ICLR 2021,Oral	Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified t...	https://openreview.net/pdf/fe47b1d2bc77e31125aa9437f2979edc98a6d02c.pdf
Growing Efficient Deep Networks by Structured Continuous Sparsification	https://openreview.net/forum?id=wb3wxCObbRT	https://openreview.net/forum?id=wb3wxCObbRT	Xin Yuan,Pedro Henrique Pamplona Savarese,Michael Maire	ICLR 2021,Oral	We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pruning or architecture search techniques that operate on full-sized models or supernet architectures, our method can start from a small, sim...	https://openreview.net/pdf/f0340388ab26e079bb52b2e75a594fa25f418c28.pdf
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments	https://openreview.net/forum?id=RmB-88r9dL	https://openreview.net/forum?id=RmB-88r9dL	Lizhen Nie,Mao Ye,qiang liu,Dan Nicolae	ICLR 2021,Oral	Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF). Available parametric methods are limited in their model space, and previous attempts in leveraging neural network to enhance model expressiveness relied on...	https://openreview.net/pdf/5c2b12078d2981db4bb6e855b2055a732e936fca.pdf
EigenGame: PCA as a Nash Equilibrium	https://openreview.net/forum?id=NzTU59SYbNq	https://openreview.net/forum?id=NzTU59SYbNq	Ian Gemp,Brian McWilliams,Claire Vernade,Thore Graepel	ICLR 2021,Oral	We present a novel view on principal components analysis as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their own utility function. We analyze the properties of this PCA game and the behavior of its gradient based updates. The resulting algorithm---which ...	https://openreview.net/pdf/05bd1b2e95cfd532a3902be824f0945943dc7503.pdf
Randomized Automatic Differentiation	https://openreview.net/forum?id=xpx9zj7CUlY	https://openreview.net/forum?id=xpx9zj7CUlY	Deniz Oktay,Nick McGreivy,Joshua Aduol,Alex Beatson,Ryan P Adams	ICLR 2021,Oral	The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical p...	https://openreview.net/pdf/e0f487101840a7b0e72a5ec77e3d0b6b57734740.pdf
A Distributional Approach to Controlled Text Generation	https://openreview.net/forum?id=jWkw45-9AbL	https://openreview.net/forum?id=jWkw45-9AbL	Muhammad Khalifa,Hady Elsahar,Marc Dymetman	ICLR 2021,Oral	We propose a Distributional Approach for addressing Controlled Text Generation from pre-trained Language Models (LM). This approach permits to specify, in a single formal framework, both “pointwise’” and “distributional” constraints over the target LM — to our knowledge, the first model with such generality —while...	https://openreview.net/pdf/11f0063ca9b22ee0ed8462004057c2417891ade2.pdf
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	https://openreview.net/forum?id=YicbFdNTTy	https://openreview.net/forum?id=YicbFdNTTy	Alexey Dosovitskiy,Lucas Beyer,Alexander Kolesnikov,Dirk Weissenborn,Xiaohua Zhai,Thomas Unterthiner,Mostafa Dehghani,Matthias Minderer,Georg Heigold,Sylvain Gelly,Jakob Uszkoreit,Neil Houlsby	ICLR 2021,Oral	While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping ...	https://openreview.net/pdf/a4aa24aed16fcb6f23d1067f1a5ecf47d7115f63.pdf
Getting a CLUE: A Method for Explaining Uncertainty Estimates	https://openreview.net/forum?id=XSLF1XFq5h	https://openreview.net/forum?id=XSLF1XFq5h	Javier Antoran,Umang Bhatt,Tameem Adel,Adrian Weller,José Miguel Hernández-Lobato	ICLR 2021,Oral	Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bay...	https://openreview.net/pdf/bb1896f36e6eb8c78e3080ebea185ff4537fc95b.pdf
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime	https://openreview.net/forum?id=PULSD5qI2N1	https://openreview.net/forum?id=PULSD5qI2N1	Atsushi Nitanda,Taiji Suzuki	ICLR 2021,Oral	We analyze the convergence of the averaged stochastic gradient descent for overparameterized two-layer neural networks for regression problems. It was recently found that a neural tangent kernel (NTK) plays an important role in showing the global convergence of gradient-based methods under the NTK regime, where the lea...	https://openreview.net/pdf/70618f9539e2b69bad45f4ed3eab3fb18f352bbf.pdf
Learning Invariant Representations for Reinforcement Learning without Reconstruction	https://openreview.net/forum?id=-2FCwDKRREu	https://openreview.net/forum?id=-2FCwDKRREu	Amy Zhang,Rowan Thomas McAllister,Roberto Calandra,Yarin Gal,Sergey Levine	ICLR 2021,Oral	We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details. Bisimulatio...	https://openreview.net/pdf/603fb2b2d3c728fdb6a0d1f23a60748227ff46c7.pdf
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability	https://openreview.net/forum?id=dYeAHXnpWJ4	https://openreview.net/forum?id=dYeAHXnpWJ4	Suraj Srinivas,Francois Fleuret	ICLR 2021,Oral	Current methods for the interpretability of discriminative deep neural networks commonly rely on the model's input-gradients, i.e., the gradients of the output logits w.r.t. the inputs. The common assumption is that these input-gradients contain information regarding $p_{\theta} ( y\mid \mathbf{x} )$, the model's discr...	https://openreview.net/pdf/b29e31cf78e011a59f9e49950670211d4c516b00.pdf
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments	https://openreview.net/forum?id=cPZOyoDloxl	https://openreview.net/forum?id=cPZOyoDloxl	Glen Berseth,Daniel Geng,Coline Manon Devin,Nicholas Rhinehart,Chelsea Finn,Dinesh Jayaraman,Sergey Levine	ICLR 2021,Oral	Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement ...	https://openreview.net/pdf/f9be390d21352320ef34c706df775ded52b79ebd.pdf
Learning to Reach Goals via Iterated Supervised Learning	https://openreview.net/forum?id=rALA0Xo6yNJ	https://openreview.net/forum?id=rALA0Xo6yNJ	Dibya Ghosh,Abhishek Gupta,Ashwin Reddy,Justin Fu,Coline Manon Devin,Benjamin Eysenbach,Sergey Levine	ICLR 2021,Oral	Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitation learning provides a simple and stable alternative, it requires access to demonstrations from a human supervisor. In this paper, we study...	https://openreview.net/pdf/34b6d953408a7aaff3549569738b80162e8e6dbc.pdf
Self-training For Few-shot Transfer Across Extreme Task Differences	https://openreview.net/forum?id=O3Y56aqpChA	https://openreview.net/forum?id=O3Y56aqpChA	Cheng Perng Phoo,Bharath Hariharan	ICLR 2021,Oral	Most few-shot learning techniques are pre-trained on a large, labeled “base dataset”. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different “source” problem domain (e.g., ImageNet), which can be very differ...	https://openreview.net/pdf/1b36075cafa13e37ec6b9a3796f921d4d587ac0a.pdf
Federated Learning Based on Dynamic Regularization	https://openreview.net/forum?id=B7v4QMR6Z9w	https://openreview.net/forum?id=B7v4QMR6Z9w	Durmus Alp Emre Acar,Yue Zhao,Ramon Matas,Matthew Mattina,Paul Whatmough,Venkatesh Saligrama	ICLR 2021,Oral	We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round. We view Federated Learning problem primarily from a communication perspective and allow more device level computations to s...	https://openreview.net/pdf/d5d8224518e951c4f83d7ee7338ee5862ea09a04.pdf
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator	https://openreview.net/forum?id=Mk6PZtgAgfq	https://openreview.net/forum?id=Mk6PZtgAgfq	Max B Paulus,Chris J. Maddison,Andreas Krause	ICLR 2021,Oral	Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance. To counteract this, modern estimators either introduce bias, rely on multiple function evaluations, or use learned, input-dependent baselines. Thus, there is a need...	https://openreview.net/pdf/99dded8f5b416c9596f112afe789c337af877339.pdf
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies	https://openreview.net/forum?id=F3s69XzWOia	https://openreview.net/forum?id=F3s69XzWOia	T. Konstantin Rusch,Siddhartha Mishra	ICLR 2021,Oral	Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators. Inspired by the ability of these systems to express a rich set of outputs while keeping (gradients of) state variables bounded, we propose a novel architecture for recurrent neural networks. O...	https://openreview.net/pdf/e69f77961c03aeefb3d2dfcc2c77185ac11781db.pdf
DiffWave: A Versatile Diffusion Model for Audio Synthesis	https://openreview.net/forum?id=a-xFK8Ymz5J	https://openreview.net/forum?id=a-xFK8Ymz5J	Zhifeng Kong,Wei Ping,Jiaji Huang,Kexin Zhao,Bryan Catanzaro	ICLR 2021,Oral	In this work, we propose DiffWave, a versatile diffusion probabilistic model for conditional and unconditional waveform generation. The model is non-autoregressive, and converts the white noise signal into structured waveform through a Markov chain with a constant number of steps at synthesis. It is efficiently trained...	https://openreview.net/pdf/d27840fc3a835c4da4a9d13c4227c7a0d8a9b3c5.pdf
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency	https://openreview.net/forum?id=QIRlze3I6hX	https://openreview.net/forum?id=QIRlze3I6hX	Qiang Zhang,Tete Xiao,Alexei A Efros,Lerrel Pinto,Xiaolong Wang	ICLR 2021,Oral	At the heart of many robotics problems is the challenge of learning correspondences across domains. For instance, imitation learning requires obtaining correspondence between humans and robots; sim-to-real requires correspondence between physics simulators and real hardware; transfer learning requires correspondences b...	https://openreview.net/pdf/2d895dd1e0137048768cefacc5fe9d81daa35d58.pdf
Deformable DETR: Deformable Transformers for End-to-End Object Detection	https://openreview.net/forum?id=gZ9hCDWe6ke	https://openreview.net/forum?id=gZ9hCDWe6ke	Xizhou Zhu,Weijie Su,Lewei Lu,Bin Li,Xiaogang Wang,Jifeng Dai	ICLR 2021,Oral	DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To ...	https://openreview.net/pdf/758d4b5c0d63033d526ff8744d872a03543bb674.pdf
Learning Generalizable Visual Representations via Interactive Gameplay	https://openreview.net/forum?id=UuchYL8wSZo	https://openreview.net/forum?id=UuchYL8wSZo	Luca Weihs,Aniruddha Kembhavi,Kiana Ehsani,Sarah M Pratt,Winson Han,Alvaro Herrasti,Eric Kolve,Dustin Schwenk,Roozbeh Mottaghi,Ali Farhadi	ICLR 2021,Oral	A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization. Comparatively little is known regarding...	https://openreview.net/pdf/9136e8ff7bc8b8e82f73830b816f60fe258ec628.pdf
Gradient Projection Memory for Continual Learning	https://openreview.net/forum?id=3AOj0RCNC2	https://openreview.net/forum?id=3AOj0RCNC2	Gobinda Saha,Isha Garg,Kaushik Roy	ICLR 2021,Oral	The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propo...	https://openreview.net/pdf/a65e5f689852fad25dc117881988232e0f95ed52.pdf
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting	https://openreview.net/forum?id=kmG8vRXTFv	https://openreview.net/forum?id=kmG8vRXTFv	Yuan Yin,Vincent LE GUEN,Jérémie DONA,Emmanuel de Bezenac,Ibrahim Ayed,Nicolas THOME,patrick gallinari	ICLR 2021,Oral	Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, ...	https://openreview.net/pdf/26f8f5665ae13d683ef87f2ff8c2e5a321fac386.pdf
Human-Level Performance in No-Press Diplomacy via Equilibrium Search	https://openreview.net/forum?id=0-uUGPbIjD	https://openreview.net/forum?id=0-uUGPbIjD	Jonathan Gray,Adam Lerer,Anton Bakhtin,Noam Brown	ICLR 2021,Oral	Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings. In contrast, Diplomacy is a game of shifting alliances that involves both cooperation and competition. For this reason, Diplomacy has proven to be a formidable research challenge. In this paper we descr...	https://openreview.net/pdf/59e1f6ceb25265194013bc67945a7001ef36ca84.pdf
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks	https://openreview.net/forum?id=UH-cmocLJC	https://openreview.net/forum?id=UH-cmocLJC	Keyulu Xu,Mozhi Zhang,Jingling Li,Simon Shaolei Du,Ken-Ichi Kawarabayashi,Stefanie Jegelka	ICLR 2021,Oral	We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolat...	https://openreview.net/pdf/eb776544f1a0835881289e4dad2436f38f88269d.pdf
Rethinking Attention with Performers	https://openreview.net/forum?id=Ua6zuk0WRH	https://openreview.net/forum?id=Ua6zuk0WRH	Krzysztof Marcin Choromanski,Valerii Likhosherstov,David Dohan,Xingyou Song,Andreea Gane,Tamas Sarlos,Peter Hawkins,Jared Quincy Davis,Afroz Mohiuddin,Lukasz Kaiser,David Benjamin Belanger,Lucy J Colwell,Adrian Weller	ICLR 2021,Oral	We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-ker...	https://openreview.net/pdf/f9985b6b0f77c997ffb932a86a3f3ff482aaa30d.pdf
What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study	https://openreview.net/forum?id=nIAxjsniDzg	https://openreview.net/forum?id=nIAxjsniDzg	Marcin Andrychowicz,Anton Raichuk,Piotr Stańczyk,Manu Orsini,Sertan Girgin,Raphaël Marinier,Leonard Hussenot,Matthieu Geist,Olivier Pietquin,Marcin Michalski,Sylvain Gelly,Olivier Bachem	ICLR 2021,Oral	In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple, their state-of-the-art implementations take numerous low- and high-level design decisions that strongly affect the performance of the resulting agents...	https://openreview.net/pdf/6e07c0b9828c97445a9667bf90fe254fa7de8e3a.pdf
Neural Synthesis of Binaural Speech From Mono Audio	https://openreview.net/forum?id=uAX8q61EVRu	https://openreview.net/forum?id=uAX8q61EVRu	Alexander Richard,Dejan Markovic,Israel D. Gebru,Steven Krenn,Gladstone Alexander Butler,Fernando Torre,Yaser Sheikh	ICLR 2021,Oral	We present a neural rendering approach for binaural sound synthesis that can produce realistic and spatially accurate binaural sound in realtime. The network takes, as input, a single-channel audio source and synthesizes, as output, two-channel binaural sound, conditioned on the relative position and orientation of the...	https://openreview.net/pdf/1d9f3bd5393a688e9b4ad20e8f71e32c0ff119e8.pdf
Dataset Condensation with Gradient Matching	https://openreview.net/forum?id=mSAKhLYLSsl	https://openreview.net/forum?id=mSAKhLYLSsl	Bo Zhao,Konda Reddy Mopuri,Hakan Bilen	ICLR 2021,Oral	As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense large datase...	https://openreview.net/pdf/f6babe705b5d43a6a88ee3b553c209341b63a5e0.pdf
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes	https://openreview.net/forum?id=HajQFbx_yB	https://openreview.net/forum?id=HajQFbx_yB	Mike Gartrell,Insu Han,Elvis Dohmatob,Jennifer Gillenwater,Victor-Emmanuel Brunel	ICLR 2021,Oral	Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection. Recent work shows that nonsymmetric DPP (NDPP) kernels have significant advantages over symmetric kernels in terms of modeling power and predictive perform...	https://openreview.net/pdf/8986ce3acb4c64c6f02ae25ebddca92808aac43c.pdf
Geometry-aware Instance-reweighted Adversarial Training	https://openreview.net/forum?id=iAX0l6Cz8ub	https://openreview.net/forum?id=iAX0l6Cz8ub	Jingfeng Zhang,Jianing Zhu,Gang Niu,Bo Han,Masashi Sugiyama,Mohan Kankanhalli	ICLR 2021,Oral	In adversarial machine learning, there was a common belief that robustness and accuracy hurt each other. The belief was challenged by recent studies where we can maintain the robustness and improve the accuracy. However, the other direction, whether we can keep the accuracy and improve the robustness, is conceptually a...	https://openreview.net/pdf/746bc7119712b56c0426659dcb575f5864fa29ec.pdf
Free Lunch for Few-shot Learning: Distribution Calibration	https://openreview.net/forum?id=JWOiYxMG92s	https://openreview.net/forum?id=JWOiYxMG92s	Shuo Yang,Lu Liu,Min Xu	ICLR 2021,Oral	Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient exam...	https://openreview.net/pdf/657c8cfa6bb8f5a10bf00d0e87e08b924e17fcf6.pdf
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding	https://openreview.net/forum?id=EbIDjBynYJ8	https://openreview.net/forum?id=EbIDjBynYJ8	David A. Klindt,Lukas Schott,Yash Sharma,Ivan Ustyuzhaninov,Wieland Brendel,Matthias Bethge,Dylan Paiton	ICLR 2021,Oral	Disentangling the underlying generative factors from complex data has so far been limited to carefully constructed scenarios. We propose a path towards natural data by first showing that the statistics of natural data provide enough structure to enable disentanglement, both theoretically and empirically. Specifically, ...	https://openreview.net/pdf/70b2f88dcd46ac3a433a562c71a9d82d8dffb042.pdf
Do 2D GANs Know 3D Shape? Unsupervised 3D Shape Reconstruction from 2D Image GANs	https://openreview.net/forum?id=FGqiDsBUKL0	https://openreview.net/forum?id=FGqiDsBUKL0	Xingang Pan,Bo Dai,Ziwei Liu,Chen Change Loy,Ping Luo	ICLR 2021,Oral	Natural images are projections of 3D objects on a 2D image plane. While state-of-the-art 2D generative models like GANs show unprecedented quality in modeling the natural image manifold, it is unclear whether they implicitly capture the underlying 3D object structures. And if so, how could we exploit such knowledge to ...	https://openreview.net/pdf/ced9a36d7c1942426cc47913c3ec31367b60dad3.pdf
Net-DNF: Effective Deep Modeling of Tabular Data	https://openreview.net/forum?id=73WTGs96kho	https://openreview.net/forum?id=73WTGs96kho	Liran Katzir,Gal Elidan,Ran El-Yaniv	ICLR 2021,Poster	A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present Net-DNF a novel ge...	https://openreview.net/pdf/0faa3d246b873caa0ff0a42200c685558260733c.pdf
Predicting Inductive Biases of Pre-Trained Models	https://openreview.net/forum?id=mNtmhaDkAr	https://openreview.net/forum?id=mNtmhaDkAr	Charles Lovering,Rohan Jha,Tal Linzen,Ellie Pavlick	ICLR 2021,Poster	Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand wh...	https://openreview.net/pdf/5f8e7508b216ea50a36e7f4584e4e6d8953917be.pdf
Optimism in Reinforcement Learning with Generalized Linear Function Approximation	https://openreview.net/forum?id=CBmJwzneppz	https://openreview.net/forum?id=CBmJwzneppz	Yining Wang,Ruosong Wang,Simon Shaolei Du,Akshay Krishnamurthy	ICLR 2021,Poster	We design a new provably efficient algorithm for episodic reinforcement learning with generalized linear function approximation. We analyze the algorithm under a new expressivity assumption that we call ``optimistic closure,'' which is strictly weaker than assumptions from prior analyses for the linear setting. With op...	https://openreview.net/pdf/a233880f2576e4e435aefcdb4dce873e39557072.pdf
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing	https://openreview.net/forum?id=oyZxhRI2RiE	https://openreview.net/forum?id=oyZxhRI2RiE	Tao Yu,Rui Zhang,Alex Polozov,Christopher Meek,Ahmed Hassan Awadallah	ICLR 2021,Poster	Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e.g., SQL, SPARQL) that can be executed against a structured ontology (e.g. databases, knowledge bases). To accomplish this task, a CSP system needs to model the relation between the ...	https://openreview.net/pdf/5f0fb4ec7db7adfa1bbec5ca4828380eed47533f.pdf
A teacher-student framework to distill future trajectories	https://openreview.net/forum?id=ECuvULjFQia	https://openreview.net/forum?id=ECuvULjFQia	Alexander Neitz,Giambattista Parascandolo,Bernhard Schölkopf	ICLR 2021,Poster	By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Mode...	https://openreview.net/pdf/92e8f1e3d126b2094e7c7fd20bede53365fc6ea9.pdf
Certify or Predict: Boosting Certified Robustness with Compositional Architectures	https://openreview.net/forum?id=USCNapootw	https://openreview.net/forum?id=USCNapootw	Mark Niklas Mueller,Mislav Balunovic,Martin Vechev	ICLR 2021,Poster	A core challenge with existing certified defense mechanisms is that while they improve certified robustness, they also tend to drastically decrease natural accuracy, making it difficult to use these methods in practice. In this work, we propose a new architecture which addresses this challenge and enables one to boost ...	https://openreview.net/pdf/9e6eb18330c25797cf3af7f9a23a4ac3a44de56c.pdf
On the Transfer of Disentangled Representations in Realistic Settings	https://openreview.net/forum?id=8VXvj1QNRl1	https://openreview.net/forum?id=8VXvj1QNRl1	Andrea Dittadi,Frederik Träuble,Francesco Locatello,Manuel Wuthrich,Vaibhav Agrawal,Ole Winther,Stefan Bauer,Bernhard Schölkopf	ICLR 2021,Poster	Learning meaningful representations that disentangle the underlying structure of the data generating process is considered to be of key importance in machine learning. While disentangled representations were found to be useful for diverse tasks such as abstract reasoning and fair classification, their scalability and r...	https://openreview.net/pdf/bd4ae699aba426a89c027ef66f9f0cc2dcb0e187.pdf
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary	https://openreview.net/forum?id=sCZbhBvqQaU	https://openreview.net/forum?id=sCZbhBvqQaU	Huan Zhang,Hongge Chen,Duane S Boning,Cho-Jui Hsieh	ICLR 2021,Poster	We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, w...	https://openreview.net/pdf/9a0def4f4b70bbb3d4c3157a3ee5e4110bb9363a.pdf
Learning from others' mistakes: Avoiding dataset biases without modeling them	https://openreview.net/forum?id=Hf3qXoiNkR	https://openreview.net/forum?id=Hf3qXoiNkR	Victor Sanh,Thomas Wolf,Yonatan Belinkov,Alexander M Rush	ICLR 2021,Poster	State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases...	https://openreview.net/pdf/85a08fe172298a34963413165b7313bc68c4656a.pdf
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers	https://openreview.net/forum?id=nVZtXBI6LNn	https://openreview.net/forum?id=nVZtXBI6LNn	Kaidi Xu,Huan Zhang,Shiqi Wang,Yihan Wang,Suman Jana,Xue Lin,Cho-Jui Hsieh	ICLR 2021,Poster	Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programm...	https://openreview.net/pdf/e8ea009d8faf5987887d2dc7ca2b4d680a2f83dc.pdf
Self-supervised Adversarial Robustness for the Low-label, High-data Regime	https://openreview.net/forum?id=bgQek2O63w	https://openreview.net/forum?id=bgQek2O63w	Sven Gowal,Po-Sen Huang,Aaron van den Oord,Timothy Mann,Pushmeet Kohli	ICLR 2021,Poster	Recent work discovered that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. Perhaps more surprisingly, these larger datasets can be "mostly" unlabeled. Pseudo-labeling, a technique simultaneously pioneered by four separ...	https://openreview.net/pdf/f29096120961de72d4ebfd2f2060c34d17b2df3c.pdf
Modeling the Second Player in Distributionally Robust Optimization	https://openreview.net/forum?id=ZDnzZrTqU9N	https://openreview.net/forum?id=ZDnzZrTqU9N	Paul Michel,Tatsunori Hashimoto,Graham Neubig	ICLR 2021,Poster	Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set"). This is done by solving a min-max game: the model is trained to minimize its maximum expected loss among all distribut...	https://openreview.net/pdf/e31aab007b357e40ee683c0d59d6d939a70c3b2a.pdf
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering	https://openreview.net/forum?id=JFKR3WqwyXR	https://openreview.net/forum?id=JFKR3WqwyXR	Calypso Herrera,Florian Krach,Josef Teichmann	ICLR 2021,Poster	Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregul...	https://openreview.net/pdf/56667371832a16b261e1e17afd3b1d2f6e9bfb0e.pdf
Gradient Origin Networks	https://openreview.net/forum?id=0O_cQfw6uEh	https://openreview.net/forum?id=0O_cQfw6uEh	Sam Bond-Taylor,Chris G. Willcocks	ICLR 2021,Poster	This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder. This is achieved using empirical Bayes to calculate the expectation of the posterior, which is implemented by initialising a latent vector with zeros, then using the gradient of the log-likelihoo...	https://openreview.net/pdf/afd0b57d3c44292d9b9855c9f88b7f8f152fc9dd.pdf
Efficient Generalized Spherical CNNs	https://openreview.net/forum?id=rWZz3sJfCkm	https://openreview.net/forum?id=rWZz3sJfCkm	Oliver Cobb,Christopher G. R. Wallis,Augustine N. Mavor-Parker,Augustin Marignier,Matthew A. Price,Mayeul d'Avezac,Jason McEwen	ICLR 2021,Poster	Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to ...	https://openreview.net/pdf/ac9b9f7c6e9cdda26ee96fc1eb656465da6950e6.pdf
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION	https://openreview.net/forum?id=XPZIaotutsD	https://openreview.net/forum?id=XPZIaotutsD	Pengcheng He,Xiaodong Liu,Jianfeng Gao,Weizhu Chen	ICLR 2021,Poster	Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techn...	https://openreview.net/pdf/283448c4c3318a56c7bb21743019e9938f252538.pdf
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning	https://openreview.net/forum?id=-6vS_4Kfz0	https://openreview.net/forum?id=-6vS_4Kfz0	Shauharda Khadka,Estelle Aflalo,Mattias Marder,Avrech Ben-David,Santiago Miret,Shie Mannor,Tamir Hazan,Hanlin Tang,Somdeb Majumdar	ICLR 2021,Poster	For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is critical to performance. The growing complexity of neural networks calls for automated memory mapping instead of manual heuristic approaches; ye...	https://openreview.net/pdf/04f5290361838504c7aedc9907d8058042fe455e.pdf
On the geometry of generalization and memorization in deep neural networks	https://openreview.net/forum?id=V8jrrnwGbuc	https://openreview.net/forum?id=V8jrrnwGbuc	Cory Stephenson,suchismita padhy,Abhinav Ganesh,Yue Hui,Hanlin Tang,SueYeon Chung	ICLR 2021,Poster	Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memorization occurs in a deep network, we use a recently developed replica-based mean field theoretic geometric analysis method. We find that all ...	https://openreview.net/pdf/4c875dc13f634270639a752914300c6c90ea6977.pdf
Continual learning in recurrent neural networks	https://openreview.net/forum?id=8xeBUgD8u9	https://openreview.net/forum?id=8xeBUgD8u9	Benjamin Ehret,Christian Henning,Maria Cervera,Alexander Meulemans,Johannes Von Oswald,Benjamin F Grewe	ICLR 2021,Poster	While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL metho...	https://openreview.net/pdf/c8abb8aebd4518ff958e3cde3bb8c972fecd7cba.pdf
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching	https://openreview.net/forum?id=01olnfLIbD	https://openreview.net/forum?id=01olnfLIbD	Jonas Geiping,Liam H Fowl,W. Ronny Huang,Wojciech Czaja,Gavin Taylor,Michael Moeller,Tom Goldstein	ICLR 2021,Poster	Data Poisoning attacks modify training data to maliciously control a model trained on such data. In this work, we focus on targeted poisoning attacks which cause a reclassification of an unmodified test image and as such breach model integrity. We consider a particularly malicious poisoning attack that is both ``from s...	https://openreview.net/pdf/3a3c570da85848de52605f6669aae395d063027b.pdf
Overfitting for Fun and Profit: Instance-Adaptive Data Compression	https://openreview.net/forum?id=oFp8Mx_V5FL	https://openreview.net/forum?id=oFp8Mx_V5FL	Ties van Rozendaal,Iris AM Huijben,Taco Cohen	ICLR 2021,Poster	Neural data compression has been shown to outperform classical methods in terms of $RD$ performance, with results still improving rapidly. At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is us...	https://openreview.net/pdf/fb86b8ef98e296045da88b73a31804ed7b488f77.pdf
A Block Minifloat Representation for Training Deep Neural Networks	https://openreview.net/forum?id=6zaTwpNSsQ2	https://openreview.net/forum?id=6zaTwpNSsQ2	Sean Fox,Seyedramin Rasoulinezhad,Julian Faraone,david boland,Philip Leong	ICLR 2021,Poster	Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating-point representations and commercially available hardware. Specialized arithmetic with custom acceleration offers perhaps the most promising alternative. Ongoing research is trending towards narrow floating-point r...	https://openreview.net/pdf/7ed682ba5c220f98e96984a0b3bb08ed91c59ce7.pdf
Representation Learning via Invariant Causal Mechanisms	https://openreview.net/forum?id=9p2ekP904Rs	https://openreview.net/forum?id=9p2ekP904Rs	Jovana Mitrovic,Brian McWilliams,Jacob C Walker,Lars Holger Buesing,Charles Blundell	ICLR 2021,Poster	Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of t...	https://openreview.net/pdf/34eb2506b5a0b489bced58ab4bb038ff7356ade7.pdf
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization	https://openreview.net/forum?id=D_KeYoqCYC	https://openreview.net/forum?id=D_KeYoqCYC	Joshua C Chang,Patrick Fletcher,Jungmin Han,Ted L Chang,Shashaank Vattikuti,Bart Desmet,Ayah Zirikly,Carson C Chow	ICLR 2021,Poster	Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods ar...	https://openreview.net/pdf/8dec97cd769e293780630c02e65abf11b85674b1.pdf
Mapping the Timescale Organization of Neural Language Models	https://openreview.net/forum?id=J3OUycKwz-	https://openreview.net/forum?id=J3OUycKwz-	Hsiang-Yun Sherry Chien,Jinhan Zhang,Christopher Honey	ICLR 2021,Poster	In the human brain, sequences of language input are processed within a distributed and hierarchical architecture, in which higher stages of processing encode contextual information over longer timescales. In contrast, in recurrent neural networks which perform natural language processing, we know little about how the m...	https://openreview.net/pdf/13cd0af93335a87135e32f629677ce93f146f5bb.pdf
Neural networks with late-phase weights	https://openreview.net/forum?id=C0qJUx5dxFb	https://openreview.net/forum?id=C0qJUx5dxFb	Johannes Von Oswald,Seijin Kobayashi,Joao Sacramento,Alexander Meulemans,Christian Henning,Benjamin F Grewe	ICLR 2021,Poster	The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a si...	https://openreview.net/pdf/b8fee003035442b69f0891687d5afc8ab42545d4.pdf
Uncertainty-aware Active Learning for Optimal Bayesian Classifier	https://openreview.net/forum?id=Mu2ZxFctAI	https://openreview.net/forum?id=Mu2ZxFctAI	Guang Zhao,Edward Dougherty,Byung-Jun Yoon,Francis Alexander,Xiaoning Qian	ICLR 2021,Poster	For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function. In Bayesian classification, expected Loss Reduction~(ELR) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step-look...	https://openreview.net/pdf/afb9e3d9e377ad46e8edc01c8ca00e45787831f8.pdf
ResNet After All: Neural ODEs and Their Numerical Solution	https://openreview.net/forum?id=HxzSxSxLOJZ	https://openreview.net/forum?id=HxzSxSxLOJZ	Katharina Ott,Prateek Katiyar,Philipp Hennig,Michael Tiemann	ICLR 2021,Poster	A key appeal of the recently proposed Neural Ordinary Differential Equation (ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural networks. As we show herein, though, trained Neural ODE models actually depend on the specific numerical method used during training. If the tr...	https://openreview.net/pdf/b059cce0ad22c05dea6c731336b7fb6d893edab1.pdf
Generalized Variational Continual Learning	https://openreview.net/forum?id=_IM-AfFhna9	https://openreview.net/forum?id=_IM-AfFhna9	Noel Loo,Siddharth Swaroop,Richard E Turner	ICLR 2021,Poster	Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL)....	https://openreview.net/pdf/75e7423995a4eb4d239591596556bd1ac05f5e63.pdf
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models	https://openreview.net/forum?id=a2gqxKDvYys	https://openreview.net/forum?id=a2gqxKDvYys	Justin Bayer,Maximilian Soelch,Atanas Mirchev,Baris Kayalibay,Patrick van der Smagt	ICLR 2021,Poster	Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only in...	https://openreview.net/pdf/3b8adcab7340a271fe4624eabaaa9168eb0c8899.pdf
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning	https://openreview.net/forum?id=SK7A5pdrgov	https://openreview.net/forum?id=SK7A5pdrgov	Ossama Ahmed,Frederik Träuble,Anirudh Goyal,Alexander Neitz,Manuel Wuthrich,Yoshua Bengio,Bernhard Schölkopf,Stefan Bauer	ICLR 2021,Poster	Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we proposeCausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environm...	https://openreview.net/pdf/824ca65f541287b48a971348ef2dff33ffce0ffd.pdf
Transformer protein language models are unsupervised structure learners	https://openreview.net/forum?id=fylclEqgvgd	https://openreview.net/forum?id=fylclEqgvgd	Roshan Rao,Joshua Meier,Tom Sercu,Sergey Ovchinnikov,Alexander Rives	ICLR 2021,Poster	Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design. For decades, the predominant approach has been to infer evolutionary constraints from a set of related sequences. In the past year, protein language models have emerg...	https://openreview.net/pdf/df3dd5269b8d22f21bb9984deb359af57f4dc0e6.pdf
Neural ODE Processes	https://openreview.net/forum?id=27acGyyI1BY	https://openreview.net/forum?id=27acGyyI1BY	Alexander Norcliffe,Cristian Bodnar,Ben Day,Jacob Moss,Pietro Liò	ICLR 2021,Poster	Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental...	https://openreview.net/pdf/cd93774612deb1de0bb3d59b6ecfd4411fbac85f.pdf
The role of Disentanglement in Generalisation	https://openreview.net/forum?id=qbH974jKUVy	https://openreview.net/forum?id=qbH974jKUVy	Milton Llera Montero,Casimir JH Ludwig,Rui Ponte Costa,Gaurav Malhotra,Jeffrey Bowers	ICLR 2021,Poster	Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It is claimed that such...	https://openreview.net/pdf/b2891a422f7bcfc82a95c0587ba5da7e42c473db.pdf
Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units	https://openreview.net/forum?id=eU776ZYxEpz	https://openreview.net/forum?id=eU776ZYxEpz	Jonathan Cornford,Damjan Kalajdzievski,Marco Leite,Amélie Lamarquette,Dimitri Michael Kullmann,Blake Aaron Richards	ICLR 2021,Poster	The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures th...	https://openreview.net/pdf/4ec70d1b966600fcb426c0ea0982e93dd870f226.pdf
SALD: Sign Agnostic Learning with Derivatives	https://openreview.net/forum?id=7EDgLu9reQD	https://openreview.net/forum?id=7EDgLu9reQD	Matan Atzmon,Yaron Lipman	ICLR 2021,Poster	Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or unoriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications. In this paper, we introduce SALD: a method for learning implicit neural representations of shapes directly from raw ...	https://openreview.net/pdf/8ee2bee9e961323ee6b93586fd34377a074e8889.pdf
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks	https://openreview.net/forum?id=TaYhv-q1Xit	https://openreview.net/forum?id=TaYhv-q1Xit	Christian H.X. Ali Mehmeti-Göpel,David Hartmann,Michael Wand	ICLR 2021,Poster	In this paper, we apply harmonic distortion analysis to understand the effect of nonlinearities in the spectral domain. Each nonlinear layer creates higher-frequency harmonics, which we call "blueshift", whose magnitude increases with network depth, thereby increasing the “roughness” of the output landscape. Unlike dif...	https://openreview.net/pdf/e4c6e52caf84c69fd30b6bfc6f0936b70f6187a9.pdf
CoCon: A Self-Supervised Approach for Controlled Text Generation	https://openreview.net/forum?id=VD_ozqvBy4W	https://openreview.net/forum?id=VD_ozqvBy4W	Alvin Chan,Yew-Soon Ong,Bill Pung,Aston Zhang,Jie Fu	ICLR 2021,Poster	Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LMs is getting attention. While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, ...	https://openreview.net/pdf/1a1a4e037209eca1776c37a85dff0459bdcad6e8.pdf
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech	https://openreview.net/forum?id=o3iritJHLfO	https://openreview.net/forum?id=o3iritJHLfO	Yoonhyung Lee,Joongbo Shin,Kyomin Jung	ICLR 2021,Poster	Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive architectures have several limitations: (1) They require a lot of time to generate a mel-spectrogram consisting of hundreds of steps. (2) The autoregressive speech generation shows a lack o...	https://openreview.net/pdf/db03d769745da96b32be80606e358d64a7641d2b.pdf
Learning continuous-time PDEs from sparse data with graph neural networks	https://openreview.net/forum?id=aUX5Plaq7Oy	https://openreview.net/forum?id=aUX5Plaq7Oy	Valerii Iakovlev,Markus Heinonen,Harri Lähdesmäki	ICLR 2021,Poster	The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discrete-time approximations or make the limiting assumption of the observations arr...	https://openreview.net/pdf/65985773a2d3e7c1e7d121a48729c30c9a007745.pdf
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition	https://openreview.net/forum?id=CU0APx9LMaL	https://openreview.net/forum?id=CU0APx9LMaL	Abhinav Mehrotra,Alberto Gil C. P. Ramos,Sourav Bhattacharya,Łukasz Dudziak,Ravichander Vipperla,Thomas Chau,Mohamed S Abdelfattah,Samin Ishtiaq,Nicholas Donald Lane	ICLR 2021,Poster	Powered by innovations in novel architecture design, noise tolerance techniques and increasing model capacity, Automatic Speech Recognition (ASR) has made giant strides in reducing word-error-rate over the past decade. ASR models are often trained with tens of thousand hours of high quality speech data to produce state...	https://openreview.net/pdf/77fdff265261021a568029907ab02c0f1f6e4639.pdf
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks	https://openreview.net/forum?id=ULQdiUTHe3y	https://openreview.net/forum?id=ULQdiUTHe3y	Jan Schuchardt,Aleksandar Bojchevski,Johannes Gasteiger,Stephan Günnemann	ICLR 2021,Poster	In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a vector of labels) based on a single input, i.e. a single graph, image, or document respectively. Existing adversarial robustness certificates consider each predict...	https://openreview.net/pdf/23b393f2d9e53b0dd38356295f14b0980ac27b18.pdf
Adversarially Guided Actor-Critic	https://openreview.net/forum?id=_mQp5cr_iNy	https://openreview.net/forum?id=_mQp5cr_iNy	Yannis Flet-Berliac,Johan Ferret,Olivier Pietquin,Philippe Preux,Matthieu Geist	ICLR 2021,Poster	Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respecti...	https://openreview.net/pdf/14e743d36a73f6572d0a514054121457cc066814.pdf
Training independent subnetworks for robust prediction	https://openreview.net/forum?id=OGg9XnKxFAH	https://openreview.net/forum?id=OGg9XnKxFAH	Marton Havasi,Rodolphe Jenatton,Stanislav Fort,Jeremiah Zhe Liu,Jasper Snoek,Balaji Lakshminarayanan,Andrew Mingbo Dai,Dustin Tran	ICLR 2021,Poster	Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant runtime cost. In ...	https://openreview.net/pdf/685ca0ecb292949cd9ede19a51143b8505e9b854.pdf
Grounding Language to Autonomously-Acquired Skills via Goal Generation	https://openreview.net/forum?id=chPj_I5KMHG	https://openreview.net/forum?id=chPj_I5KMHG	Ahmed Akakzia,Cédric Colas,Pierre-Yves Oudeyer,Mohamed CHETOUANI,Olivier Sigaud	ICLR 2021,Poster	We are interested in the autonomous acquisition of repertoires of skills. Language-conditioned reinforcement learning (LC-RL) approaches are great tools in this quest, as they allow to express abstract goals as sets of constraints on the states. However, most LC-RL agents are not autonomous and cannot learn without ext...	https://openreview.net/pdf/d347c6eea558834f6b578284633cc4f3f1a0c0a9.pdf
Hopfield Networks is All You Need	https://openreview.net/forum?id=tL89RnzIiCd	https://openreview.net/forum?id=tL89RnzIiCd	Hubert Ramsauer,Bernhard Schäfl,Johannes Lehner,Philipp Seidl,Michael Widrich,Lukas Gruber,Markus Holzleitner,Thomas Adler,David Kreil,Michael K Kopp,Günter Klambauer,Johannes Brandstetter,Sepp Hochreiter	ICLR 2021,Poster	We introduce a modern Hopfield network with continuous states and a corresponding update rule. The new Hopfield network can store exponentially (with the dimension of the associative space) many patterns, retrieves the pattern with one update, and has exponentially small retrieval errors. It has three types of energy m...	https://openreview.net/pdf/4dfbed3a6ececb7282dfef90fd6c03812ae0da7b.pdf
Differentiable Trust Region Layers for Deep Reinforcement Learning	https://openreview.net/forum?id=qYZD-AO1Vn	https://openreview.net/forum?id=qYZD-AO1Vn	Fabian Otto,Philipp Becker,Vien Anh Ngo,Hanna Carolin Maria Ziesche,Gerhard Neumann	ICLR 2021,Poster	Trust region methods are a popular tool in reinforcement learning as they yield robust policy updates in continuous and discrete action spaces. However, enforcing such trust regions in deep reinforcement learning is difficult. Hence, many approaches, such as Trust Region Policy Optimization (TRPO) and Proximal Policy O...	https://openreview.net/pdf/16d7f0bd8f02047e8ca8dbcf7a7f000f5d685020.pdf

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ICLR 2021 International Conference on Learning Representations 2021 Accepted Paper Meta Info Dataset

This dataset is collect from the ICLR 2021 OpenReview website (https://openreview.net/group?id=ICLR.cc/2021/Conference#tab-accept-oral) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/iclr2021). For researchers who are interested in doing analysis of ICLR 2021 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the ICLR 2021 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.

Meta Information of Json File

{
    "title": "On the mapping between Hopfield networks and Restricted Boltzmann Machines",
    "url": "https://openreview.net/forum?id=RGJbergVIoO",
    "detail_url": "https://openreview.net/forum?id=RGJbergVIoO",
    "authors": "Matthew Smart,Anton Zilman",
    "tags": "ICLR 2021,Oral",
    "abstract": "Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact mapping between HNs and RBMs has been previously noted for the special case of orthogonal (\u201cuncorrelated\u201d) encoded patterns. We present here an exact mapping in the case of correlated pattern HNs, which are more broadly applicable to existing datasets. Specifically, we show that any HN with $N$ binary variables and $p<N$ potentially correlated binary patterns can be transformed into an RBM with $N$ binary visible variables and $p$ gaussian hidden variables. We outline the conditions under which the reverse mapping exists, and conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. We discuss extensions, the potential importance of this correspondence for the training of RBMs, and for understanding the performance of feature extraction methods which utilize RBMs.",
    "pdf": "https://openreview.net/pdf/3a9204f4495810f86acf886d14ee022a31d7b863.pdf"
}

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ICLR 2021 International Conference on Learning Representations 2021 Accepted Paper Meta Info Dataset

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