paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
jHefDGsorp5 | Molecule Optimization by Explainable Evolution | https://openreview.net/forum?id=jHefDGsorp5 | [
"Binghong Chen",
"Tianzhe Wang",
"Chengtao Li",
"Hanjun Dai",
"Le Song"
] | Poster | null | Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science, and drug discovery. This paper develops a novel algorithm for optimizing molecular properties via an Expectation-Maximization (EM) like explainable evolutionary process. The algorithm is designed to mimic h... | [
"Molecule Design",
"Explainable Model",
"Evolutionary Algorithm",
"Reinforcement Learning",
"Graph Generative Model"
] | null | 2,246 | null | null |
VcB4QkSfyO | Estimating Lipschitz constants of monotone deep equilibrium models | https://openreview.net/forum?id=VcB4QkSfyO | [
"Chirag Pabbaraju",
"Ezra Winston",
"J Zico Kolter"
] | Poster | null | Several methods have been proposed in recent years to provide bounds on the Lipschitz constants of deep networks, which can be used to provide robustness guarantees, generalization bounds, and characterize the smoothness of decision boundaries. However, existing bounds get substantially weaker with increasing depth of ... | [
"deep equilibrium models",
"Lipschitz constants"
] | null | 2,234 | null | null |
3q5IqUrkcF | Implicit Gradient Regularization | https://openreview.net/forum?id=3q5IqUrkcF | [
"David Barrett",
"Benoit Dherin"
] | Poster | null | Gradient descent can be surprisingly good at optimizing deep neural networks without overfitting and without explicit regularization. We find that the discrete steps of gradient descent implicitly regularize models by penalizing gradient descent trajectories that have large loss gradients. We call this Implicit Gradien... | [
"implicit regularization",
"deep learning",
"deep learning theory",
"theoretical issues in deep learning",
"theory",
"regularization"
] | null | 2,231 | 2009.11162 | title_snapshot |
US-TP-xnXI | Structured Prediction as Translation between Augmented Natural Languages | https://openreview.net/forum?id=US-TP-xnXI | [
"Giovanni Paolini",
"Ben Athiwaratkun",
"Jason Krone",
"Jie Ma",
"Alessandro Achille",
"RISHITA ANUBHAI",
"Cicero Nogueira dos Santos",
"Bing Xiang",
"Stefano Soatto"
] | Spotlight | null | We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue ... | [
"language models",
"few-shot learning",
"transfer learning",
"structured prediction",
"generative modeling",
"sequence to sequence",
"multi-task learning"
] | null | 2,229 | 2101.05779 | title_snapshot |
YCXrx6rRCXO | Faster Binary Embeddings for Preserving Euclidean Distances | https://openreview.net/forum?id=YCXrx6rRCXO | [
"Jinjie Zhang",
"Rayan Saab"
] | Poster | null | We propose a fast, distance-preserving, binary embedding algorithm to transform a high-dimensional dataset $\mathcal{T}\subseteq\mathbb{R}^n$ into binary sequences in the cube $\{\pm 1\}^m$. When $\mathcal{T}$ consists of well-spread (i.e., non-sparse) vectors, our embedding method applies a stable noise-shaping quanti... | [
"Binary Embeddings",
"Johnson-Lindenstrauss Transforms",
"Sigma Delta Quantization"
] | null | 2,225 | 2010.00712 | title_snapshot |
23ZjUGpjcc | Scalable Transfer Learning with Expert Models | https://openreview.net/forum?id=23ZjUGpjcc | [
"Joan Puigcerver",
"Carlos Riquelme Ruiz",
"Basil Mustafa",
"Cedric Renggli",
"André Susano Pinto",
"Sylvain Gelly",
"Daniel Keysers",
"Neil Houlsby"
] | Poster | null | Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with... | [
"Transfer Learning",
"Expert Models",
"Few Shot"
] | null | 2,224 | 2009.13239 | title_snapshot |
lqU2cs3Zca | Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU | https://openreview.net/forum?id=lqU2cs3Zca | [
"Patrick Kidger",
"Terry Lyons"
] | Poster | null | Signatory is a library for calculating and performing functionality related to the signature and logsignature transforms. The focus is on machine learning, and as such includes features such as CPU parallelism, GPU support, and backpropagation. To our knowledge it is the first GPU-capable library for these operations. ... | [
"signature",
"logsignature",
"gpu",
"library",
"open source"
] | null | 2,220 | 2001.00706 | title_snapshot |
IFqrg1p5Bc | Distance-Based Regularisation of Deep Networks for Fine-Tuning | https://openreview.net/forum?id=IFqrg1p5Bc | [
"Henry Gouk",
"Timothy Hospedales",
"Massimiliano Pontil"
] | Poster | null | We investigate approaches to regularisation during fine-tuning of deep neural networks. First we provide a neural network generalisation bound based on Rademacher complexity that uses the distance the weights have moved from their initial values. This bound has no direct dependence on the number of weights and compares... | [
"Deep Learning",
"Transfer Learning",
"Statistical Learning Theory"
] | null | 2,216 | 2002.08253 | title_snapshot |
iWLByfvUhN | Decoupling Global and Local Representations via Invertible Generative Flows | https://openreview.net/forum?id=iWLByfvUhN | [
"Xuezhe Ma",
"Xiang Kong",
"Shanghang Zhang",
"Eduard H Hovy"
] | Poster | null | In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative flow in the VAE framework to model the decoder.
Specifically, the proposed model utilizes the variational auto-encoding fr... | [
"Generative Models",
"Generative Flow",
"Normalizing Flow",
"Image Generation",
"Representation Learning"
] | null | 2,214 | 2004.11820 | title_snapshot |
C3qvk5IQIJY | Understanding Over-parameterization in Generative Adversarial Networks | https://openreview.net/forum?id=C3qvk5IQIJY | [
"Yogesh Balaji",
"Mohammadmahdi Sajedi",
"Neha Mukund Kalibhat",
"Mucong Ding",
"Dominik Stöger",
"Mahdi Soltanolkotabi",
"Soheil Feizi"
] | Poster | null | A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold. Indeed, most successful GANs used in practice are trained using overparameterized generator and discrim... | [
"GAN",
"Over-parameterization",
"min-max optimization"
] | null | 2,213 | null | null |
A2gNouoXE7 | Filtered Inner Product Projection for Crosslingual Embedding Alignment | https://openreview.net/forum?id=A2gNouoXE7 | [
"Vin Sachidananda",
"Ziyi Yang",
"Chenguang Zhu"
] | Poster | null | Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space. Recently, these algorithms have been studied in the setting of bilingual lexicon induction where one seeks to align the embeddings of a source and a ta... | [
"multilingual representations",
"word embeddings",
"natural language processing"
] | null | 2,212 | 2006.03652 | title_snapshot |
YUGG2tFuPM | Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks | https://openreview.net/forum?id=YUGG2tFuPM | [
"Alexander Levine",
"Soheil Feizi"
] | Poster | null | Adversarial poisoning attacks distort training data in order to corrupt the test-time behavior of a classifier. A provable defense provides a certificate for each test sample, which is a lower bound on the magnitude of any adversarial distortion of the training set that can corrupt the test sample's classification.
We ... | [
"bagging",
"ensemble",
"robustness",
"certificate",
"poisoning",
"smoothing"
] | null | 2,210 | 2006.14768 | title_judge |
wb3wxCObbRT | Growing Efficient Deep Networks by Structured Continuous Sparsification | https://openreview.net/forum?id=wb3wxCObbRT | [
"Xin Yuan",
"Pedro Henrique Pamplona Savarese",
"Michael Maire"
] | Oral | null | 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... | [
"deep learning",
"computer vision",
"network pruning",
"neural architecture search"
] | null | 2,209 | 2007.15353 | title_snapshot |
9GBZBPn0Jx | HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents | https://openreview.net/forum?id=9GBZBPn0Jx | [
"Deyao Zhu",
"Mohamed Zahran",
"Li Erran Li",
"Mohamed Elhoseiny"
] | Poster | null | Motion forecasting is essential for making intelligent decisions in robotic navigation. As a result, the multi-agent behavioral prediction has become a core component of modern human-robot interaction applications such as autonomous driving. Due to various intentions and interactions among agents, agent trajectories ca... | [] | null | 2,202 | null | null |
O6LPudowNQm | INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving | https://openreview.net/forum?id=O6LPudowNQm | [
"Yuhuai Wu",
"Albert Jiang",
"Jimmy Ba",
"Roger Baker Grosse"
] | Poster | null | In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark designed to test agents’ generalization ability. INT is based on a theorem generator, which provides theoret... | [
"Theorem proving",
"Synthetic benchmark dataset",
"Generalization",
"Transformers",
"Graph neural networks",
"Monte Carlo Tree Search"
] | null | 2,194 | 2007.02924 | title_snapshot |
lgNx56yZh8a | Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes | https://openreview.net/forum?id=lgNx56yZh8a | [
"Jake Snell",
"Richard Zemel"
] | Poster | null | Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning. Bayesian methods are well-suited to tackling the fundamental issue of overfitting in the few-shot scenario because they allow practitione... | [
"few-shot learning",
"gaussian processes",
"bayesian deep learning",
"uncertainty estimation"
] | null | 2,193 | 2007.10417 | title_snapshot |
l35SB-_raSQ | A Hypergradient Approach to Robust Regression without Correspondence | https://openreview.net/forum?id=l35SB-_raSQ | [
"Yujia Xie",
"Yixiu Mao",
"Simiao Zuo",
"Hongteng Xu",
"Xiaojing Ye",
"Tuo Zhao",
"Hongyuan Zha"
] | Poster | null | We consider a regression problem, where the correspondence between the input and output data is not available. Such shuffled data are commonly observed in many real world problems. Take flow cytometry as an example: the measuring instruments are unable to preserve the correspondence between the samples and the measurem... | [
"Regression without correspondence",
"differentiable programming",
"first-order optimization",
"Sinkhorn algorithm"
] | null | 2,185 | 2012.00123 | title_snapshot |
W3Wf_wKmqm9 | C-Learning: Horizon-Aware Cumulative Accessibility Estimation | https://openreview.net/forum?id=W3Wf_wKmqm9 | [
"Panteha Naderian",
"Gabriel Loaiza-Ganem",
"Harry J. Braviner",
"Anthony L. Caterini",
"Jesse C. Cresswell",
"Tong Li",
"Animesh Garg"
] | Poster | null | Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer from three major challenges: high sample complexity, learning only a single way of reaching the goals, and difficulties in solving complex... | [
"reinforcement learning",
"goal reaching",
"Q-learning"
] | null | 2,177 | 2011.12363 | title_snapshot |
O-XJwyoIF-k | Minimum Width for Universal Approximation | https://openreview.net/forum?id=O-XJwyoIF-k | [
"Sejun Park",
"Chulhee Yun",
"Jaeho Lee",
"Jinwoo Shin"
] | Spotlight | null | The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks. However, the critical width enabling the universal approximation has not been exactly characterized in terms of the input dimension $d_x$ and the output dimen... | [
"universal approximation",
"neural networks"
] | null | 2,176 | 2006.08859 | title_snapshot |
W1G1JZEIy5_ | MIROSTAT: A NEURAL TEXT DECODING ALGORITHM THAT DIRECTLY CONTROLS PERPLEXITY | https://openreview.net/forum?id=W1G1JZEIy5_ | [
"Sourya Basu",
"Govardana Sachitanandam Ramachandran",
"Nitish Shirish Keskar",
"Lav R. Varshney"
] | Poster | null | Neural text decoding algorithms strongly influence the quality of texts generated using language models, but popular algorithms like top-k, top-p (nucleus), and temperature-based sampling may yield texts that have objectionable repetition or incoherence. Although these methods generate high-quality text after ad hoc pa... | [
"Neural text decoding",
"sampling algorithms",
"cross-entropy",
"repetitions",
"incoherence"
] | null | 2,175 | 2007.14966 | title_snapshot |
bB2drc7DPuB | Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime | https://openreview.net/forum?id=bB2drc7DPuB | [
"Andrea Agazzi",
"Jianfeng Lu"
] | Poster | null | We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms. We concentrate on the training dynamics in the mean-field regime, modeling e.g. the behavior of wide single hidden layer... | [
"policy gradient",
"entropy regularization",
"mean-field dynamics",
"neural networks"
] | null | 2,174 | 2010.11858 | title_snapshot |
guetrIHLFGI | The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers | https://openreview.net/forum?id=guetrIHLFGI | [
"Preetum Nakkiran",
"Behnam Neyshabur",
"Hanie Sedghi"
] | Poster | null | We propose a new framework for reasoning about generalization in deep learning.
The core idea is to couple the Real World, where optimizers take stochastic gradient steps on the empirical loss, to an Ideal World, where optimizers take steps on the population loss. This leads to an alternate decomposition of test error... | [
"generalization",
"optimization",
"online learning",
"understanding deep learning",
"empirical investigation"
] | null | 2,172 | 2010.08127 | title_snapshot |
vYVI1CHPaQg | A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning | https://openreview.net/forum?id=vYVI1CHPaQg | [
"Samuel Horváth",
"Peter Richtarik"
] | Poster | null | Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computing systems. A key bottleneck of such systems is the communication overhead for exchanging information across the workers, such as stochastic gradients. Among the many techniques proposed t... | [
"distributed optimization",
"communication efficiency"
] | null | 2,162 | 2006.11077 | title_snapshot |
9FWas6YbmB3 | DrNAS: Dirichlet Neural Architecture Search | https://openreview.net/forum?id=9FWas6YbmB3 | [
"Xiangning Chen",
"Ruochen Wang",
"Minhao Cheng",
"Xiaocheng Tang",
"Cho-Jui Hsieh"
] | Poster | null | This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing weight as random variables, modeled by Dirichlet distribution. With recently developed pathwise derivatives, the Dirichlet parameters can be... | [] | null | 2,159 | 2006.10355 | title_snapshot |
FmMKSO4e8JK | Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation | https://openreview.net/forum?id=FmMKSO4e8JK | [
"Justin Fu",
"Sergey Levine"
] | Poster | null | In this work we consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectu... | [
"model-based optimization",
"normalized maximum likelihood"
] | null | 2,157 | 2102.07970 | title_snapshot |
enoVQWLsfyL | Viewmaker Networks: Learning Views for Unsupervised Representation Learning | https://openreview.net/forum?id=enoVQWLsfyL | [
"Alex Tamkin",
"Mike Wu",
"Noah Goodman"
] | Poster | null | Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input. However, designing these views requires considerable trial and error by human experts, hindering widespread adoption of unsupervised representation learning methods across d... | [
"unsupervised learning",
"self-supervised",
"representation learning",
"contrastive learning",
"views",
"data augmentation"
] | null | 2,156 | 2010.07432 | title_snapshot |
vXj_ucZQ4hA | Robust Pruning at Initialization | https://openreview.net/forum?id=vXj_ucZQ4hA | [
"Soufiane Hayou",
"Jean-Francois Ton",
"Arnaud Doucet",
"Yee Whye Teh"
] | Poster | null | Overparameterized Neural Networks (NN) display state-of-the-art performance. However, there is a growing need for smaller, energy-efficient, neural networks to be able to use machine learning applications on devices with limited computational resources. A popular approach consists of using pruning techniques. While the... | [
"Pruning",
"Initialization",
"Compression"
] | null | 2,155 | 2002.08797 | title_snapshot |
CHLhSw9pSw8 | Single-Photon Image Classification | https://openreview.net/forum?id=CHLhSw9pSw8 | [
"Thomas Fischbacher",
"Luciano Sbaiz"
] | Poster | null | Quantum Computing based Machine Learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature. We demonstrate the existence of a "quantum computing toy model" that illustrates key aspects of quantum information pro... | [
"quantum mechanics",
"image classification",
"quantum machine learning",
"theoretical limits"
] | null | 2,148 | 2008.05859 | title_snapshot |
xfmSoxdxFCG | Can a Fruit Fly Learn Word Embeddings? | https://openreview.net/forum?id=xfmSoxdxFCG | [
"Yuchen Liang",
"Chaitanya Ryali",
"Benjamin Hoover",
"Leopold Grinberg",
"Saket Navlakha",
"Mohammed J Zaki",
"Dmitry Krotov"
] | Poster | null | The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representa... | [
"neurobiology",
"neuroscience",
"fruit fly",
"locality sensitive hashing",
"word embedding",
"sparse representations"
] | null | 2,145 | 2101.06887 | title_snapshot |
RmB-88r9dL | VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments | https://openreview.net/forum?id=RmB-88r9dL | [
"Lizhen Nie",
"Mao Ye",
"qiang liu",
"Dan Nicolae"
] | Oral | null | 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... | [
"causal inference",
"continuous treatment effect",
"doubly robustness"
] | null | 2,143 | 2103.07861 | title_snapshot |
LGgdb4TS4Z | Topology-Aware Segmentation Using Discrete Morse Theory | https://openreview.net/forum?id=LGgdb4TS4Z | [
"Xiaoling Hu",
"Yusu Wang",
"Li Fuxin",
"Dimitris Samaras",
"Chao Chen"
] | Spotlight | null | In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel connectivity and membrane closure, is crucial for downstream analysis tasks. In this paper, we propose a new approach to train deep image segment... | [
"Topology",
"Morse theory",
"Image segmentation"
] | null | 2,141 | 2103.09992 | title_snapshot |
ajOrOhQOsYx | A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels | https://openreview.net/forum?id=ajOrOhQOsYx | [
"Leon Lang",
"Maurice Weiler"
] | Poster | null | Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance. Recent advances in the theoretical description of GCNNs revealed that such models can generally be understood as performing convolutions with $G... | [
"Group Equivariant Convolution",
"Steerable Kernel",
"Quantum Mechanics",
"Wigner-Eckart Theorem",
"Representation Theory",
"Harmonic Analysis",
"Peter-Weyl Theorem"
] | null | 2,140 | 2010.10952 | title_snapshot |
dKg5D1Z1Lm | Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds | https://openreview.net/forum?id=dKg5D1Z1Lm | [
"Yihao Feng",
"Ziyang Tang",
"na zhang",
"qiang liu"
] | Poster | null | Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data collection is expensive... | [
"Non-asymptotic Confidence Intervals",
"Off Policy Evaluation",
"Reinforcement Learnings"
] | null | 2,135 | 2103.05741 | title_snapshot |
9YlaeLfuhJF | Model Patching: Closing the Subgroup Performance Gap with Data Augmentation | https://openreview.net/forum?id=9YlaeLfuhJF | [
"Karan Goel",
"Albert Gu",
"Yixuan Li",
"Christopher Re"
] | Poster | null | Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage. To mitigate these performance differences, we i... | [
"Robust Machine Learning",
"Data Augmentation",
"Consistency Training",
"Invariant Representations"
] | null | 2,132 | 2008.06775 | title_snapshot |
fw-BHZ1KjxJ | SOLAR: Sparse Orthogonal Learned and Random Embeddings | https://openreview.net/forum?id=fw-BHZ1KjxJ | [
"Tharun Medini",
"Beidi Chen",
"Anshumali Shrivastava"
] | Poster | null | Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. However, the bottleneck of indexing a large number of dense vectors and performing an NNS hurts the ... | [
"Sparse Embedding",
"Inverted Index",
"Learning to Hash",
"Embedding Models"
] | null | 2,128 | 2008.13225 | title_snapshot |
snOgiCYZgJ7 | Neural representation and generation for RNA secondary structures | https://openreview.net/forum?id=snOgiCYZgJ7 | [
"Zichao Yan",
"William L. Hamilton",
"Mathieu Blanchette"
] | Poster | null | Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The design of large scale and complex biological structures spurs dedicated graph-based deep generative modeling techniques, wh... | [
"Graph neural network",
"Deep generative modeling",
"Machine learning",
"Drug discovery",
"RNA structure",
"RNA structure embedding",
"RNA-protein interaction prediction"
] | null | 2,126 | 2102.00925 | title_snapshot |
vVjIW3sEc1s | A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks | https://openreview.net/forum?id=vVjIW3sEc1s | [
"Nikunj Saunshi",
"Sadhika Malladi",
"Sanjeev Arora"
] | Poster | null | Autoregressive language models, pretrained using large text corpora to do well on next word prediction, have been successful at solving many downstream tasks, even with zero-shot usage. However, there is little theoretical understanding of this success. This paper initiates a mathematical study of this phenomenon for t... | [
"language models",
"theory",
"representation learning",
"self-supervised learning",
"unsupervised learning",
"transfer learning",
"natural language processing"
] | null | 2,122 | 2010.03648 | title_snapshot |
NzTU59SYbNq | EigenGame: PCA as a Nash Equilibrium | https://openreview.net/forum?id=NzTU59SYbNq | [
"Ian Gemp",
"Brian McWilliams",
"Claire Vernade",
"Thore Graepel"
] | Oral | null | 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 ... | [
"pca",
"principal components analysis",
"nash",
"games",
"eigendecomposition",
"svd",
"singular value decomposition"
] | null | 2,114 | 2010.00554 | title_snapshot |
gl3D-xY7wLq | Noise or Signal: The Role of Image Backgrounds in Object Recognition | https://openreview.net/forum?id=gl3D-xY7wLq | [
"Kai Yuanqing Xiao",
"Logan Engstrom",
"Andrew Ilyas",
"Aleksander Madry"
] | Poster | null | We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misc... | [
"Backgrounds",
"Model Biases",
"Robustness",
"Computer Vision"
] | null | 2,112 | 2006.09994 | title_snapshot |
yUxUNaj2Sl | Does enhanced shape bias improve neural network robustness to common corruptions? | https://openreview.net/forum?id=yUxUNaj2Sl | [
"Chaithanya Kumar Mummadi",
"Ranjitha Subramaniam",
"Robin Hutmacher",
"Julien Vitay",
"Volker Fischer",
"Jan Hendrik Metzen"
] | Poster | null | Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen... | [
"neural network robustness",
"shape bias",
"corruptions",
"distribution shift"
] | null | 2,111 | 2104.09789 | title_snapshot |
LXMSvPmsm0g | Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning | https://openreview.net/forum?id=LXMSvPmsm0g | [
"Tianlong Chen",
"Zhenyu Zhang",
"Sijia Liu",
"Shiyu Chang",
"Zhangyang Wang"
] | Poster | null | The lottery ticket hypothesis states that a highly sparsified sub-network can be trained in isolation, given the appropriate weight initialization. This paper extends that hypothesis from one-shot task learning, and demonstrates for the first time that such extremely compact and independently trainable sub-networks can... | [
"lottery tickets",
"winning tickets",
"lifelong learning"
] | null | 2,110 | null | null |
MjvduJCsE4 | Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit | https://openreview.net/forum?id=MjvduJCsE4 | [
"Ben Adlam",
"Jaehoon Lee",
"Lechao Xiao",
"Jeffrey Pennington",
"Jasper Snoek"
] | Poster | null | Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor uncertainty estimates, such as overconfidence on out-of-distribution (OOD) data and ungraceful failing under distributional shift. Previous b... | [
"Deep Learning",
"Uncertainty",
"Infinite-Width Limit",
"Neural Network Gaussian Process",
"Bayesian Neural Networks",
"Gaussian Process"
] | null | 2,105 | 2010.07355 | title_snapshot |
TK_6nNb_C7q | Hierarchical Autoregressive Modeling for Neural Video Compression | https://openreview.net/forum?id=TK_6nNb_C7q | [
"Ruihan Yang",
"Yibo Yang",
"Joseph Marino",
"Stephan Mandt"
] | Poster | null | Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models. We draw a connection between such autoregressive generative models and the task of lossy video compression. Specifically, we view recent neu... | [
"Compression",
"Video Compression",
"Generative Models",
"Autoregressive Models"
] | null | 2,103 | 2010.10258 | title_snapshot |
ujmgfuxSLrO | DeLighT: Deep and Light-weight Transformer | https://openreview.net/forum?id=ujmgfuxSLrO | [
"Sachin Mehta",
"Marjan Ghazvininejad",
"Srinivasan Iyer",
"Luke Zettlemoyer",
"Hannaneh Hajishirzi"
] | Poster | null | We introduce a deep and light-weight transformer, DeLighT, that delivers similar or better performance than standard transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block using the DeLighT transformation, a deep and light-weigh... | [
"Transformers",
"Sequence Modeling",
"Machine Translation",
"Language Modeling",
"Representation learning",
"Efficient Networks"
] | null | 2,100 | 2008.00623 | title_snapshot |
nkIDwI6oO4_ | Learning A Minimax Optimizer: A Pilot Study | https://openreview.net/forum?id=nkIDwI6oO4_ | [
"Jiayi Shen",
"Xiaohan Chen",
"Howard Heaton",
"Tianlong Chen",
"Jialin Liu",
"Wotao Yin",
"Zhangyang Wang"
] | Poster | null | Solving continuous minimax optimization is of extensive practical interest, yet notoriously unstable and difficult. This paper introduces the learning to optimize(L2O) methodology to the minimax problems for the first time and addresses its accompanying unique challenges. We first present Twin-L2O, the first dedicated ... | [
"Learning to Optimize",
"Minimax Optimization"
] | null | 2,099 | null | null |
zElset1Klrp | Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online | https://openreview.net/forum?id=zElset1Klrp | [
"Yangchen Pan",
"Kirby Banman",
"Martha White"
] | Poster | null | Recent work has shown that sparse representations---where only a small percentage of units are active---can significantly reduce interference. Those works, however, relied on relatively complex regularization or meta-learning approaches, that have only been used offline in a pre-training phase. In this work, we pursue ... | [
"Reinforcement learning",
"natural sparsity",
"sparse representation",
"fuzzy tiling activation function"
] | null | 2,083 | 1911.08068 | title_snapshot |
dgtpE6gKjHn | FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | https://openreview.net/forum?id=dgtpE6gKjHn | [
"Hong-You Chen",
"Wei-Lun Chao"
] | Poster | null | Federated learning aims to collaboratively train a strong global model by accessing users' locally trained models but not their own data. A crucial step is therefore to aggregate local models into a global model, which has been shown challenging when users have non-i.i.d. data. In this paper, we propose a novel aggrega... | [] | null | 2,078 | 2009.01974 | title_snapshot |
xpx9zj7CUlY | Randomized Automatic Differentiation | https://openreview.net/forum?id=xpx9zj7CUlY | [
"Deniz Oktay",
"Nick McGreivy",
"Joshua Aduol",
"Alex Beatson",
"Ryan P Adams"
] | Oral | null | 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... | [
"automatic differentiation",
"autodiff",
"backprop",
"deep learning",
"pdes",
"stochastic optimization"
] | null | 2,076 | 2007.10412 | title_snapshot |
7uVcpu-gMD | Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | https://openreview.net/forum?id=7uVcpu-gMD | [
"Róbert Csordás",
"Sjoerd van Steenkiste",
"Jürgen Schmidhuber"
] | Poster | null | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide in... | [
"modularity",
"systematic generalization",
"compositionality"
] | null | 2,069 | 2010.02066 | title_snapshot |
-bxf89v3Nx | Calibration tests beyond classification | https://openreview.net/forum?id=-bxf89v3Nx | [
"David Widmann",
"Fredrik Lindsten",
"Dave Zachariah"
] | Poster | null | Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point estimates. Such models can be a valuable tool in decision-making under unc... | [
"calibration",
"uncertainty quantification",
"framework",
"integral probability metric",
"maximum mean discrepancy"
] | null | 2,065 | 2210.13355 | title_snapshot |
3jjmdp7Hha | Meta Back-Translation | https://openreview.net/forum?id=3jjmdp7Hha | [
"Hieu Pham",
"Xinyi Wang",
"Yiming Yang",
"Graham Neubig"
] | Poster | null | Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data. However, several recent works have found that better translation quality in the pseudo-parallel data does not necessarily lead to a better final translation model, while lower-qua... | [
"meta learning",
"machine translation",
"back translation"
] | null | 2,063 | 2102.07847 | title_snapshot |
vujTf_I8Kmc | Attentional Constellation Nets for Few-Shot Learning | https://openreview.net/forum?id=vujTf_I8Kmc | [
"Weijian Xu",
"yifan xu",
"Huaijin Wang",
"Zhuowen Tu"
] | Poster | null | The success of deep convolutional neural networks builds on top of the learning of effective convolution operations, capturing a hierarchy of structured features via filtering, activation, and pooling. However, the explicit structured features, e.g. object parts, are not expressive in the existing CNN frameworks. In th... | [
"few-shot learning",
"constellation models"
] | null | 2,058 | null | null |
jN5y-zb5Q7m | Uncertainty Estimation in Autoregressive Structured Prediction | https://openreview.net/forum?id=jN5y-zb5Q7m | [
"Andrey Malinin",
"Mark Gales"
] | Poster | null | Uncertainty estimation is important for ensuring safety and robustness of AI systems. While most research in the area has focused on un-structured prediction tasks, limited work has investigated general uncertainty estimation approaches for structured prediction. Thus, this work aims to investigate uncertainty estimat... | [
"ensembles",
"structures prediction",
"uncertainty estimation",
"knowledge uncertainty",
"autoregressive models",
"information theory",
"machine translation",
"speech recognition."
] | null | 2,052 | 2002.07650 | title_snapshot |
d7KBjmI3GmQ | Measuring Massive Multitask Language Understanding | https://openreview.net/forum?id=d7KBjmI3GmQ | [
"Dan Hendrycks",
"Collin Burns",
"Steven Basart",
"Andy Zou",
"Mantas Mazeika",
"Dawn Song",
"Jacob Steinhardt"
] | Poster | null | We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent mode... | [
"multitask",
"few-shot"
] | null | 2,049 | 2009.03300 | title_snapshot |
ixpSxO9flk3 | No MCMC for me: Amortized sampling for fast and stable training of energy-based models | https://openreview.net/forum?id=ixpSxO9flk3 | [
"Will Sussman Grathwohl",
"Jacob Jin Kelly",
"Milad Hashemi",
"Mohammad Norouzi",
"Kevin Swersky",
"David Duvenaud"
] | Poster | null | Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. Despite recent advances, training EBMs on high-dimensional data remains a challenging problem as the state-of-the-art approaches are costly, unstable, and require considerable tuning and domain expertise to apply successfully. In ... | [
"Generative Models",
"EBM",
"Energy-Based Models",
"Energy Based Models",
"semi-supervised learning",
"JEM"
] | null | 2,046 | 2010.04230 | title_snapshot |
jWkw45-9AbL | A Distributional Approach to Controlled Text Generation | https://openreview.net/forum?id=jWkw45-9AbL | [
"Muhammad Khalifa",
"Hady Elsahar",
"Marc Dymetman"
] | Oral | null | 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... | [
"Controlled NLG",
"Pretrained Language Models",
"Bias in Language Models",
"Energy-Based Models",
"Information Geometry",
"Exponential Families"
] | null | 2,042 | 2012.11635 | title_snapshot |
dNy_RKzJacY | Aligning AI With Shared Human Values | https://openreview.net/forum?id=dNy_RKzJacY | [
"Dan Hendrycks",
"Collin Burns",
"Steven Basart",
"Andrew Critch",
"Jerry Li",
"Dawn Song",
"Jacob Steinhardt"
] | Poster | null | We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physi... | [
"value learning",
"human preferences",
"alignment"
] | null | 2,039 | 2008.02275 | title_snapshot |
WznmQa42ZAx | Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking | https://openreview.net/forum?id=WznmQa42ZAx | [
"Michael Sejr Schlichtkrull",
"Nicola De Cao",
"Ivan Titov"
] | Spotlight | null | Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. However, there has been little work on interpreting them, and specifically on understanding which parts of the graphs (e.g. syntactic trees or co-reference structures) contribute to a prediction. In t... | [
"Graph neural networks",
"interpretability",
"sparse stochastic gates",
"semantic role labeling",
"question answering"
] | null | 2,026 | 2010.00577 | title_snapshot |
7pgFL2Dkyyy | Class Normalization for (Continual)? Generalized Zero-Shot Learning | https://openreview.net/forum?id=7pgFL2Dkyyy | [
"Ivan Skorokhodov",
"Mohamed Elhoseiny"
] | Poster | null | Normalization techniques have proved to be a crucial ingredient of successful training in a traditional supervised learning regime. However, in the zero-shot learning (ZSL) world, these ideas have received only marginal attention. This work studies normalization in ZSL scenario from both theoretical and practical persp... | [
"zero-shot learning",
"normalization",
"continual learning",
"initialization"
] | null | 2,020 | 2006.11328 | title_snapshot |
hb1sDDSLbV | Learning explanations that are hard to vary | https://openreview.net/forum?id=hb1sDDSLbV | [
"Giambattista Parascandolo",
"Alexander Neitz",
"Antonio Orvieto",
"Luigi Gresele",
"Bernhard Schölkopf"
] | Poster | null | In this paper, we investigate the principle that good explanations are hard to vary in the context of deep learning.
We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and `patchwork' solutions that sew together different strategies, instead of identifying inv... | [
"invariances",
"consistency",
"gradient alignment"
] | null | 2,015 | 2009.00329 | title_snapshot |
NSBrFgJAHg | Degree-Quant: Quantization-Aware Training for Graph Neural Networks | https://openreview.net/forum?id=NSBrFgJAHg | [
"Shyam Anil Tailor",
"Javier Fernandez-Marques",
"Nicholas Donald Lane"
] | Poster | null | Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data. Despite their promise, there exists little research exploring methods to make them more efficient at inference time. In this work, we explore the viability of training ... | [
"Graph neural networks",
"quantization",
"benchmark"
] | null | 2,013 | 2008.05000 | title_snapshot |
yr1mzrH3IC | Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control | https://openreview.net/forum?id=yr1mzrH3IC | [
"Zhuang Liu",
"Xuanlin Li",
"Bingyi Kang",
"Trevor Darrell"
] | Spotlight | null | Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly becau... | [
"Policy Optimization",
"Regularization",
"Continuous Control",
"Deep Reinforcement Learning"
] | null | 2,011 | null | null |
mLcmdlEUxy- | Recurrent Independent Mechanisms | https://openreview.net/forum?id=mLcmdlEUxy- | [
"Anirudh Goyal",
"Alex Lamb",
"Jordan Hoffmann",
"Shagun Sodhani",
"Sergey Levine",
"Yoshua Bengio",
"Bernhard Schölkopf"
] | Spotlight | null | We explore the hypothesis that learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes that only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups ... | [
"modular representations",
"better generalization",
"learning mechanisms"
] | null | 2,010 | 1909.10893 | title_snapshot |
PbEHqvFtcS | Byzantine-Resilient Non-Convex Stochastic Gradient Descent | https://openreview.net/forum?id=PbEHqvFtcS | [
"Zeyuan Allen-Zhu",
"Faeze Ebrahimianghazani",
"Jerry Li",
"Dan Alistarh"
] | Poster | null | We study adversary-resilient stochastic distributed optimization, in which $m$ machines can independently compute stochastic gradients, and cooperate to jointly optimize over their local objective functions. However, an $\alpha$-fraction of the machines are Byzantine, in that they may behave in arbitrary, adversarial w... | [
"distributed machine learning",
"distributed deep learning",
"robust deep learning",
"non-convex optimization",
"Byzantine resilience"
] | null | 2,007 | 2012.14368 | title_snapshot |
dOcQK-f4byz | Teaching Temporal Logics to Neural Networks | https://openreview.net/forum?id=dOcQK-f4byz | [
"Christopher Hahn",
"Frederik Schmitt",
"Jens U. Kreber",
"Markus Norman Rabe",
"Bernd Finkbeiner"
] | Poster | null | We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the proble... | [
"Logic",
"Verification",
"Transformer"
] | null | 1,994 | 2003.04218 | title_snapshot |
7dpmlkBuJFC | Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification | https://openreview.net/forum?id=7dpmlkBuJFC | [
"Yingxue Zhou",
"Steven Wu",
"Arindam Banerjee"
] | Poster | null | Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each gradient update, the error rate of DP-SGD scales with the ambient dimension $p$, the number of parameters in the model. Such dependence can b... | [] | null | 1,989 | 2007.03813 | title_snapshot |
PpshD0AXfA | Generative Time-series Modeling with Fourier Flows | https://openreview.net/forum?id=PpshD0AXfA | [
"Ahmed Alaa",
"Alex James Chan",
"Mihaela van der Schaar"
] | Poster | null | Generating synthetic time-series data is crucial in various application domains, such as medical prognosis, wherein research is hamstrung by the lack of access to data due to concerns over privacy. Most of the recently proposed methods for generating synthetic time-series rely on implicit likelihood modeling using gene... | [] | null | 1,980 | null | null |
jphnJNOwe36 | Overparameterisation and worst-case generalisation: friend or foe? | https://openreview.net/forum?id=jphnJNOwe36 | [
"Aditya Krishna Menon",
"Ankit Singh Rawat",
"Sanjiv Kumar"
] | Poster | null | Overparameterised neural networks have demonstrated the remarkable ability to perfectly fit training samples, while still generalising to unseen test samples. However, several recent works have revealed that such models' good average performance does not always translate to good worst-case performance: in particular, t... | [
"overparameterisation",
"worst-case generalisation"
] | null | 1,977 | null | null |
TgSVWXw22FQ | Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning | https://openreview.net/forum?id=TgSVWXw22FQ | [
"Siyang Yuan",
"Pengyu Cheng",
"Ruiyi Zhang",
"Weituo Hao",
"Zhe Gan",
"Lawrence Carin"
] | Poster | null | Voice style transfer, also called voice conversion, seeks to modify one speaker's voice to generate speech as if it came from another (target) speaker. Previous works have made progress on voice conversion with parallel training data and pre-known speakers. However, zero-shot voice style transfer, which learns from non... | [
"Style Transfer",
"Mutual Information",
"Zero-shot Learning",
"Disentanglement"
] | null | 1,968 | 2103.09420 | title_snapshot |
60j5LygnmD | Meta-learning with negative learning rates | https://openreview.net/forum?id=60j5LygnmD | [
"Alberto Bernacchia"
] | Poster | null | Deep learning models require a large amount of data to perform well. When data is scarce for a target task, we can transfer the knowledge gained by training on similar tasks to quickly learn the target. A successful approach is meta-learning, or "learning to learn" a distribution of tasks, where "learning" is represent... | [
"Meta-learning"
] | null | 1,967 | 2102.00940 | title_snapshot |
PS3IMnScugk | Learning to Recombine and Resample Data For Compositional Generalization | https://openreview.net/forum?id=PS3IMnScugk | [
"Ekin Akyürek",
"Afra Feyza Akyürek",
"Jacob Andreas"
] | Poster | null | Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data—particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. gramma... | [
"compositional generalization",
"data augmentation",
"language processing",
"sequence models",
"generative modeling"
] | null | 1,966 | 2010.03706 | title_snapshot |
hvdKKV2yt7T | Dataset Inference: Ownership Resolution in Machine Learning | https://openreview.net/forum?id=hvdKKV2yt7T | [
"Pratyush Maini",
"Mohammad Yaghini",
"Nicolas Papernot"
] | Spotlight | null | With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property. This has spurred interest in model stealing, which is made more practical by advances in learning with partial, little, or no supervision. Existing defenses focus on inserting uniq... | [
"model ownership",
"model extraction",
"MLaaS"
] | null | 1,964 | 2104.10706 | title_snapshot |
4IwieFS44l | Fooling a Complete Neural Network Verifier | https://openreview.net/forum?id=4IwieFS44l | [
"Dániel Zombori",
"Balázs Bánhelyi",
"Tibor Csendes",
"István Megyeri",
"Márk Jelasity"
] | Poster | null | The efficient and accurate characterization of the robustness of neural networks to input perturbation is an important open problem. Many approaches exist including heuristic and exact (or complete) methods. Complete methods are expensive but their mathematical formulation guarantees that they provide exact robustness ... | [
"adversarial examples",
"complete verifiers",
"numerical errors"
] | null | 1,961 | null | null |
BM---bH_RSh | UMEC: Unified model and embedding compression for efficient recommendation systems | https://openreview.net/forum?id=BM---bH_RSh | [
"Jiayi Shen",
"Haotao Wang",
"Shupeng Gui",
"Jianchao Tan",
"Zhangyang Wang",
"Ji Liu"
] | Poster | null | The recommendation system (RS) plays an important role in the content recommendation and retrieval scenarios. The core part of the system is the Ranking neural network, which is usually a bottleneck of whole system performance during online inference. In this work, we propose a unified model and embedding compression ... | [
"recommendation system",
"model compression",
"ADMM",
"resource constrained"
] | null | 1,960 | null | null |
4dXmpCDGNp7 | Evaluations and Methods for Explanation through Robustness Analysis | https://openreview.net/forum?id=4dXmpCDGNp7 | [
"Cheng-Yu Hsieh",
"Chih-Kuan Yeh",
"Xuanqing Liu",
"Pradeep Kumar Ravikumar",
"Seungyeon Kim",
"Sanjiv Kumar",
"Cho-Jui Hsieh"
] | Poster | null | Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we establish a novel set of evaluation criteria for such feature based explanations by robustness analysis. In contrast to existing evaluations ... | [
"Interpretability",
"Explanations",
"Adversarial Robustness"
] | null | 1,959 | 2006.00442 | title_snapshot |
HCSgyPUfeDj | Learning and Evaluating Representations for Deep One-Class Classification | https://openreview.net/forum?id=HCSgyPUfeDj | [
"Kihyuk Sohn",
"Chun-Liang Li",
"Jinsung Yoon",
"Minho Jin",
"Tomas Pfister"
] | Poster | null | We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that ar... | [
"deep one-class classification",
"self-supervised learning"
] | null | 1,955 | 2011.02578 | title_snapshot |
1YLJDvSx6J4 | Learning from Protein Structure with Geometric Vector Perceptrons | https://openreview.net/forum?id=1YLJDvSx6J4 | [
"Bowen Jing",
"Stephan Eismann",
"Patricia Suriana",
"Raphael John Lamarre Townshend",
"Ron Dror"
] | Spotlight | null | Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the geometric and relational aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which ... | [
"structural biology",
"graph neural networks",
"proteins",
"geometric deep learning"
] | null | 1,954 | 2009.01411 | title_snapshot |
GJwMHetHc73 | Unsupervised Object Keypoint Learning using Local Spatial Predictability | https://openreview.net/forum?id=GJwMHetHc73 | [
"Anand Gopalakrishnan",
"Sjoerd van Steenkiste",
"Jürgen Schmidhuber"
] | Spotlight | null | We propose PermaKey, a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, it utilizes predictab... | [
"unsupervised representation learning",
"object-keypoint representations",
"visual saliency"
] | null | 1,953 | 2011.12930 | title_snapshot |
v8b3e5jN66j | Conditional Negative Sampling for Contrastive Learning of Visual Representations | https://openreview.net/forum?id=v8b3e5jN66j | [
"Mike Wu",
"Milan Mosse",
"Chengxu Zhuang",
"Daniel Yamins",
"Noah Goodman"
] | Poster | null | Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two transformations of an image. NCE typically uses randomly sampled negative examples to normalize the objective, but this may often include ... | [
"contrastive learning",
"hard negative mining",
"mutual information",
"lower bound",
"detection",
"segmentation",
"MoCo"
] | null | 1,947 | 2010.02037 | title_snapshot |
qpsl2dR9twy | Communication in Multi-Agent Reinforcement Learning: Intention Sharing | https://openreview.net/forum?id=qpsl2dR9twy | [
"Woojun Kim",
"Jongeui Park",
"Youngchul Sung"
] | Poster | null | Communication is one of the core components for learning coordinated behavior in multi-agent systems.
In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generat... | [
"Multi-agent reinforcement learning",
"communication",
"intention",
"attention"
] | null | 1,946 | null | null |
tkAtoZkcUnm | Neural Thompson Sampling | https://openreview.net/forum?id=tkAtoZkcUnm | [
"Weitong ZHANG",
"Dongruo Zhou",
"Lihong Li",
"Quanquan Gu"
] | Poster | null | Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson Sampling, which adapts deep neural networks for both exploration and exploitation. At the core of our algorithm is a novel posterior distri... | [
"Deep Learning",
"Contextual Bandits",
"Thompson sampling"
] | null | 1,944 | 2010.00827 | title_snapshot |
7R7fAoUygoa | Optimal Regularization can Mitigate Double Descent | https://openreview.net/forum?id=7R7fAoUygoa | [
"Preetum Nakkiran",
"Prayaag Venkat",
"Sham M. Kakade",
"Tengyu Ma"
] | Poster | null | Recent empirical and theoretical studies have shown that many learning algorithms -- from linear regression to neural networks -- can have test performance that is non-monotonic in quantities such the sample size and model size. This striking phenomenon, often referred to as "double descent", has raised questions of if... | [
"double descent",
"generalization",
"regularization",
"regression",
"monotonicity"
] | null | 1,938 | 2003.01897 | title_snapshot |
8HhkbjrWLdE | Separation and Concentration in Deep Networks | https://openreview.net/forum?id=8HhkbjrWLdE | [
"John Zarka",
"Florentin Guth",
"Stéphane Mallat"
] | Poster | null | Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training set, and increasing the Fisher discriminant ratio. We explain this mechanism with two types of operators. We prove that a rectifier without b... | [
"fisher ratio",
"neural collapse",
"mean separation",
"concentration",
"variance reduction",
"deep learning",
"image classification"
] | null | 1,937 | 2012.10424 | title_snapshot |
zWy1uxjDdZJ | Fast Geometric Projections for Local Robustness Certification | https://openreview.net/forum?id=zWy1uxjDdZJ | [
"Aymeric Fromherz",
"Klas Leino",
"Matt Fredrikson",
"Bryan Parno",
"Corina Pasareanu"
] | Spotlight | null | Local robustness ensures that a model classifies all inputs within an $\ell_p$-ball consistently, which precludes various forms of adversarial inputs.
In this paper, we present a fast procedure for checking local robustness in feed-forward neural networks with piecewise-linear activation functions.
Such networks partit... | [
"verification",
"robustness",
"safety"
] | null | 1,934 | 2002.04742 | title_snapshot |
rgFNuJHHXv | Group Equivariant Generative Adversarial Networks | https://openreview.net/forum?id=rgFNuJHHXv | [
"Neel Dey",
"Antong Chen",
"Soheil Ghafurian"
] | Poster | null | Recent improvements in generative adversarial visual synthesis incorporate real and fake image transformation in a self-supervised setting, leading to increased stability and perceptual fidelity. However, these approaches typically involve image augmentations via additional regularizers in the GAN objective and thus sp... | [
"Group Equivariance",
"Geometric Deep Learning",
"Generative Adversarial Networks"
] | null | 1,928 | 2005.01683 | title_snapshot |
hpH98mK5Puk | InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective | https://openreview.net/forum?id=hpH98mK5Puk | [
"Boxin Wang",
"Shuohang Wang",
"Yu Cheng",
"Zhe Gan",
"Ruoxi Jia",
"Bo Li",
"Jingjing Liu"
] | Poster | null | Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We aim to address this problem from an information-theoretic perspective, and ... | [
"adversarial robustness",
"information theory",
"BERT",
"adversarial training",
"NLI",
"QA"
] | null | 1,927 | 2010.02329 | title_snapshot |
QtTKTdVrFBB | Random Feature Attention | https://openreview.net/forum?id=QtTKTdVrFBB | [
"Hao Peng",
"Nikolaos Pappas",
"Dani Yogatama",
"Roy Schwartz",
"Noah Smith",
"Lingpeng Kong"
] | Spotlight | null | Transformers are state-of-the-art models for a variety of sequence modeling tasks. At their core is an attention function which models pairwise interactions between the inputs at every timestep. While attention is powerful, it does not scale efficiently to long sequences due to its quadratic time and space complexity i... | [
"Attention",
"transformers",
"machine translation",
"language modeling"
] | null | 1,925 | 2103.02143 | title_snapshot |
sjuuTm4vj0 | Using latent space regression to analyze and leverage compositionality in GANs | https://openreview.net/forum?id=sjuuTm4vj0 | [
"Lucy Chai",
"Jonas Wulff",
"Phillip Isola"
] | Poster | null | In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional pr... | [
"Image Synthesis",
"Composition",
"Generative Adversarial Networks",
"Image Editing",
"Interpretability"
] | null | 1,918 | 2103.10426 | title_snapshot |
giit4HdDNa | Go with the flow: Adaptive control for Neural ODEs | https://openreview.net/forum?id=giit4HdDNa | [
"Mathieu Chalvidal",
"Matthew Ricci",
"Rufin VanRullen",
"Thomas Serre"
] | Poster | null | Despite their elegant formulation and lightweight memory cost, neural ordinary differential equations (NODEs) suffer from known representational limitations. In particular, the single flow learned by NODEs cannot express all homeomorphisms from a given data space to itself, and their static weight parameterization rest... | [
"Neural ODEs",
"Optimal Control Theory",
"Hypernetworks",
"Normalizing flows"
] | null | 1,916 | 2006.09545 | title_snapshot |
7aL-OtQrBWD | A Learning Theoretic Perspective on Local Explainability | https://openreview.net/forum?id=7aL-OtQrBWD | [
"Jeffrey Li",
"Vaishnavh Nagarajan",
"Gregory Plumb",
"Ameet Talwalkar"
] | Poster | null | In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditional problem of performance generalization and bound the test-time predictive accuracy of a model using a notion of how locally explainable i... | [
"Interpretability",
"Learning Theory",
"Local Explanations",
"Generalization"
] | null | 1,915 | 2011.01205 | title_snapshot |
YicbFdNTTy | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | 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"
] | Oral | null | 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 ... | [
"computer vision",
"image recognition",
"self-attention",
"transformer",
"large-scale training"
] | null | 1,909 | 2010.11929 | title_snapshot |
XI-OJ5yyse | CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks | https://openreview.net/forum?id=XI-OJ5yyse | [
"Jiaqi Ma",
"Bo Chang",
"Xuefei Zhang",
"Qiaozhu Mei"
] | Poster | null | Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \textit{representational} and the \textit{correlational} roles played by the graphs in node-level prediction tasks, and we investigate how Gra... | [
"Graph Neural Network",
"Gaussian Copula",
"Gaussian Graphical Model"
] | null | 1,903 | 2010.02089 | title_snapshot |
jh-rTtvkGeM | Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability | https://openreview.net/forum?id=jh-rTtvkGeM | [
"Jeremy Cohen",
"Simran Kaur",
"Yuanzhi Li",
"J Zico Kolter",
"Ameet Talwalkar"
] | Poster | null | We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the value $2 / \text{(step size)}$, and the training loss behaves non-mono... | [
"optimization",
"trajectory",
"stability",
"sharpness",
"implicit bias",
"implicit regularization",
"L-smoothness",
"deep learning theory",
"science of deep learning"
] | null | 1,902 | 2103.00065 | title_snapshot |
SlrqM9_lyju | AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly | https://openreview.net/forum?id=SlrqM9_lyju | [
"Yuchen Jin",
"Tianyi Zhou",
"Liangyu Zhao",
"Yibo Zhu",
"Chuanxiong Guo",
"Marco Canini",
"Arvind Krishnamurthy"
] | Poster | null | The learning rate (LR) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs. However, it is also one of the least automated parts of machine learning systems and usually costs significant manual effort and computing. Though there are pre-defined LR schedules and optimizers with... | [] | null | 1,900 | 2105.10762 | title_snapshot |
kmqjgSNXby | Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization | https://openreview.net/forum?id=kmqjgSNXby | [
"Michael R Zhang",
"Thomas Paine",
"Ofir Nachum",
"Cosmin Paduraru",
"George Tucker",
"ziyu wang",
"Mohammad Norouzi"
] | Poster | null | Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state... | [
"Off-policy policy evaluation",
"autoregressive models",
"offline reinforcement learning",
"policy optimization"
] | null | 1,897 | 2104.13877 | title_snapshot |
IrM64DGB21 | On the role of planning in model-based deep reinforcement learning | https://openreview.net/forum?id=IrM64DGB21 | [
"Jessica B Hamrick",
"Abram L. Friesen",
"Feryal Behbahani",
"Arthur Guez",
"Fabio Viola",
"Sims Witherspoon",
"Thomas Anthony",
"Lars Holger Buesing",
"Petar Veličković",
"Theophane Weber"
] | Poster | null | Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made ... | [
"model-based RL",
"planning",
"MuZero"
] | null | 1,895 | 2011.04021 | title_snapshot |
0cmMMy8J5q | Zero-Cost Proxies for Lightweight NAS | https://openreview.net/forum?id=0cmMMy8J5q | [
"Mohamed S Abdelfattah",
"Abhinav Mehrotra",
"Łukasz Dudziak",
"Nicholas Donald Lane"
] | Poster | null | Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluati... | [
"NAS",
"AutoML",
"proxy",
"pruning",
"efficient"
] | null | 1,886 | 2101.08134 | title_snapshot |
ehJqJQk9cw | Personalized Federated Learning with First Order Model Optimization | https://openreview.net/forum?id=ehJqJQk9cw | [
"Michael Zhang",
"Karan Sapra",
"Sanja Fidler",
"Serena Yeung",
"Jose M. Alvarez"
] | Poster | null | While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients. Here we propose an alternative, where each client only federates with other relevant clients to obtain a stronger model per client-specific obj... | [
"Federated learning",
"personalized learning"
] | null | 1,878 | 2012.08565 | title_snapshot |
d-XzF81Wg1 | Deconstructing the Regularization of BatchNorm | https://openreview.net/forum?id=d-XzF81Wg1 | [
"Yann Dauphin",
"Ekin Dogus Cubuk"
] | Poster | null | Batch normalization (BatchNorm) has become a standard technique in deep learning. Its popularity is in no small part due to its often positive effect on generalization. Despite this success, the regularization effect of the technique is still poorly understood. This study aims to decompose BatchNorm into separate mecha... | [
"deep learning",
"batch normalization",
"regularization",
"understanding neural networks"
] | null | 1,876 | null | null |
193sEnKY1ij | No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks | https://openreview.net/forum?id=193sEnKY1ij | [
"Shyamgopal Karthik",
"Ameya Prabhu",
"Puneet K. Dokania",
"Vineet Gandhi"
] | Poster | null | There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surpri... | [
"Hierarchy-Aware Classification",
"Conditional Risk Minimization",
"Post-Hoc Correction"
] | null | 1,873 | 2104.00795 | title_snapshot |
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