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Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | https://openreview.net/forum?id=g1SzIRLQXMM | [
"Franziska Geiger",
"Martin Schrimpf",
"Tiago Marques",
"James J. DiCarlo"
] | Spotlight | null | After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are considered poor models of the development of the visual system because they posit millions of sequential, precisely coordinated ... | [
"computational neuroscience",
"primate visual ventral stream",
"convolutional neural networks",
"biologically plausible learning"
] | null | 4,724 | null | null | [
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Learning to Downsample for Segmentation of Ultra-High Resolution Images | https://openreview.net/forum?id=HndgQudNb91 | [
"Chen Jin",
"Ryutaro Tanno",
"Thomy Mertzanidou",
"Eleftheria Panagiotaki",
"Daniel C. Alexander"
] | Poster | null | Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work,... | [
"ultra-high resolution image segmentation",
"non-uniform dowmsampling",
"efficient segmentation",
"large volume image segmentation",
"medical image segmentation"
] | null | 4,722 | 2109.11071 | title_snapshot | [
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Variational Neural Cellular Automata | https://openreview.net/forum?id=7fFO4cMBx_9 | [
"Rasmus Berg Palm",
"Miguel González Duque",
"Shyam Sudhakaran",
"Sebastian Risi"
] | Poster | null | In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms --- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process.
Inspired by the incredible diversity of this biological generative process, we propose a generative model... | [
"Neural Cellular Automata",
"Cellular Automata",
"Self-Organization",
"Generative Models"
] | null | 4,721 | 2201.12360 | title_snapshot | [
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Wish you were here: Hindsight Goal Selection for long-horizon dexterous manipulation | https://openreview.net/forum?id=FKp8-pIRo3y | [
"Todor Davchev",
"Oleg Olegovich Sushkov",
"Jean-Baptiste Regli",
"Stefan Schaal",
"Yusuf Aytar",
"Markus Wulfmeier",
"Jon Scholz"
] | Poster | null | Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of ``narrow passages'' in their state space. Solving such tasks with a sparse reward in a sample-efficient manner poses a challenge to modern reinforcement learning (RL) due to the associated long-horizon nature ... | [
"goal-conditioned reinforcement learning",
"learning from demonstrations",
"long-horizon dexterous manipulation",
"bi-manual manipulation"
] | null | 4,719 | 2112.00597 | title_snapshot | [
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L0-Sparse Canonical Correlation Analysis | https://openreview.net/forum?id=KntaNRo6R48 | [
"Ofir Lindenbaum",
"Moshe Salhov",
"Amir Averbuch",
"Yuval Kluger"
] | Poster | null | Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed \textit{canonical variates} are widely used in unsupervised learning to analyze unlabeled multi-modal registered datasets. Despite their success, CCA m... | [] | null | 4,717 | null | null | [
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Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | https://openreview.net/forum?id=B7ZbqNLDn-_ | [
"Sheikh Shams Azam",
"Seyyedali Hosseinalipour",
"Qiang Qiu",
"Christopher Brinton"
] | Poster | null | In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients (i.e., the gradient-space) in centralized model training, and observe that the gradient-spac... | [
"Distributed Machine Learning",
"Federated Learning",
"Gradient Subspace",
"SGD"
] | null | 4,715 | 2202.00280 | title_snapshot | [
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Is Homophily a Necessity for Graph Neural Networks? | https://openreview.net/forum?id=ucASPPD9GKN | [
"Yao Ma",
"Xiaorui Liu",
"Neil Shah",
"Jiliang Tang"
] | Poster | null | Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption (``like attracts like''), and fail to generalize to hete... | [] | null | 4,711 | 2106.06134 | title_snapshot | [
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DEGREE: Decomposition Based Explanation for Graph Neural Networks | https://openreview.net/forum?id=Ve0Wth3ptT_ | [
"Qizhang Feng",
"Ninghao Liu",
"Fan Yang",
"Ruixiang Tang",
"Mengnan Du",
"Xia Hu"
] | Poster | null | Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximatio... | [
"XAI",
"GNN"
] | null | 4,703 | 2305.12895 | title_snapshot | [
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Improving Mutual Information Estimation with Annealed and Energy-Based Bounds | https://openreview.net/forum?id=T0B9AoM_bFg | [
"Rob Brekelmans",
"Sicong Huang",
"Marzyeh Ghassemi",
"Greg Ver Steeg",
"Roger Baker Grosse",
"Alireza Makhzani"
] | Poster | null | Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work,... | [
"mutual information estimation",
"annealed importance sampling",
"energy-based models"
] | null | 4,668 | 2303.06992 | title_snapshot | [
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Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods | https://openreview.net/forum?id=bp-LJ4y_XC | [
"Xueyuan She",
"Saurabh Dash",
"Saibal Mukhopadhyay"
] | Poster | null | A dynamical system of spiking neurons with only feedforward connections can classify spatiotemporal patterns without recurrent connections. However, the theoretical construct of a feedforward spiking neural network (SNN) for approximating a temporal sequence remains unclear, making it challenging to optimize SNN archit... | [
"spiking neural network",
"spatiotemporal processing",
"feedforward network"
] | null | 4,662 | null | null | [
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Diverse Client Selection for Federated Learning via Submodular Maximization | https://openreview.net/forum?id=nwKXyFvaUm | [
"Ravikumar Balakrishnan",
"Tian Li",
"Tianyi Zhou",
"Nageen Himayat",
"Virginia Smith",
"Jeff Bilmes"
] | Poster | null | In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them all. The optimal size of this subset is not known and several studies have shown that typically random selection does not perform very well in terms of ... | [
"federated learning",
"submodularity",
"diversity"
] | null | 4,660 | null | null | [
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From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation | https://openreview.net/forum?id=jT1EwXu-4hj | [
"Da Xu",
"Yuting Ye",
"Chuanwei Ruan",
"Evren Korpeoglu",
"Sushant Kumar",
"Kannan Achan"
] | Poster | null | The interventional nature of recommendation has attracted increasing attention in recent years. It particularly motivates researchers to formulate learning and evaluating recommendation as causal inference and data missing-not-at-random problems. However, few take seriously the consequence of violating the critical ass... | [
"Information retrieval",
"Learning theory",
"Causal inference",
"Missing data",
"Overlapping",
"Reweighting",
"Optimal transport"
] | null | 4,651 | 2203.13956 | title_snapshot | [
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Variational Predictive Routing with Nested Subjective Timescales | https://openreview.net/forum?id=JxFgJbZ-wft | [
"Alexey Zakharov",
"Qinghai Guo",
"Zafeirios Fountas"
] | Poster | null | Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore hierarchical generative models that can flexibly adapt their layerwise representations in response to datasets with different temporal dynami... | [
"Hierarchical temporal abstraction",
"event discovery",
"hierarchical generative models",
"variational inference"
] | null | 4,647 | 2110.11236 | title_snapshot | [
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Sample and Computation Redistribution for Efficient Face Detection | https://openreview.net/forum?id=RhB1AdoFfGE | [
"Jia Guo",
"Jiankang Deng",
"Alexandros Lattas",
"Stefanos Zafeiriou"
] | Poster | null | Although tremendous strides have been made in uncontrolled face detection, accurate face detection with a low computation cost remains an open challenge. In this paper, we point out that computation distribution and scale augmentation are the keys to detecting small faces from low-resolution images. Motivated by these ... | [
"efficient face detection",
"computation redistribution",
"sample redistribution"
] | null | 4,630 | 2105.04714 | title_snapshot | [
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Sound Adversarial Audio-Visual Navigation | https://openreview.net/forum?id=NkZq4OEYN- | [
"Yinfeng Yu",
"Wenbing Huang",
"Fuchun Sun",
"Changan Chen",
"Yikai Wang",
"Xiaohong Liu"
] | Poster | null | Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most real-wor... | [] | null | 4,629 | 2202.10910 | title_snapshot | [
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Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations | https://openreview.net/forum?id=12RoR2o32T | [
"Aahlad Manas Puli",
"Lily H Zhang",
"Eric Karl Oermann",
"Rajesh Ranganath"
] | Poster | null | In many prediction problems, spurious correlations are induced by a changing relationship between the label and a nuisance variable that is also correlated with the covariates. For example, in classifying animals in natural images, the background, which is a nuisance, can predict the type of animal. This nuisance-label... | [
"spurious correlations",
"out of distribution generalization",
"ml for health",
"representation learning"
] | null | 4,618 | 2107.00520 | title_snapshot | [
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Dynamics-Aware Comparison of Learned Reward Functions | https://openreview.net/forum?id=CALFyKVs87 | [
"Blake Wulfe",
"Logan Michael Ellis",
"Jean Mercat",
"Rowan Thomas McAllister",
"Adrien Gaidon"
] | Spotlight | null | The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, $\textit{comparing}$ reward functions, for example as a means of evaluating reward learning methods, presents a challenge. Reward functions are typically compared by considering the... | [
"Reward Learning",
"Inverse Reinforcement Learning",
"Reinforcement Learning",
"Comparing Reward Functions"
] | null | 4,616 | 2201.10081 | title_snapshot | [
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AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis | https://openreview.net/forum?id=OM_lYiHXiCL | [
"Junfeng Guo",
"Ang Li",
"Cong Liu"
] | Poster | null | Deep neural networks (DNNs) are proved to be vulnerable against backdoor attacks. A backdoor could be embedded in the target DNNs through injecting a backdoor trigger into the training examples, which can cause the target DNNs misclassify an input attached with the backdoor trigger. Recent backdoor detection methods o... | [] | null | 4,615 | 2110.14880 | title_snapshot | [
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Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum | https://openreview.net/forum?id=5ECQL05ub0J | [
"Kirby Banman",
"Garnet Liam Peet-Pare",
"Nidhi Hegde",
"Alona Fyshe",
"Martha White"
] | Poster | null | Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated input samples such as continual learning and reinforcement learning. Existing work has shown that SGDm with a decaying step-size can... | [
"optimization",
"momentum",
"stochastic gradient descent",
"non-iid sampling"
] | null | 4,609 | 2203.11992 | title_snapshot | [
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Domino: Discovering Systematic Errors with Cross-Modal Embeddings | https://openreview.net/forum?id=FPCMqjI0jXN | [
"Sabri Eyuboglu",
"Maya Varma",
"Khaled Kamal Saab",
"Jean-Benoit Delbrouck",
"Christopher Lee-Messer",
"Jared Dunnmon",
"James Zou",
"Christopher Re"
] | Oral | null | Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address... | [
"robustness",
"subgroup analysis",
"error analysis",
"multimodal",
"slice discovery"
] | null | 4,597 | 2203.14960 | title_snapshot | [
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Top-label calibration and multiclass-to-binary reductions | https://openreview.net/forum?id=WqoBaaPHS- | [
"Chirag Gupta",
"Aaditya Ramdas"
] | Poster | null | We propose a new notion of multiclass calibration called top-label calibration. A classifier is said to be top-label calibrated if the reported probability for the predicted class label---the top-label---is calibrated, conditioned on the top-label. This conditioning is essential for practical utility of the calibration... | [
"calibration",
"multiclass",
"uncertainty quantification",
"distribution-free",
"histogram binning"
] | null | 4,592 | 2107.08353 | title_snapshot | [
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Anisotropic Random Feature Regression in High Dimensions | https://openreview.net/forum?id=JfaWawZ8BmX | [
"Gabriel Mel",
"Jeffrey Pennington"
] | Poster | null | In contrast to standard statistical wisdom, modern learning algorithms typically find their best performance in the overparameterized regime in which the model has many more parameters than needed to fit the training data. A growing number of recent works have shown that random feature models can offer a detailed theor... | [
"random feature models",
"high dimensional asymptotics",
"generalization",
"learning curves",
"double descent",
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"alignment"
] | null | 4,589 | null | null | [
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Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future | https://openreview.net/forum?id=L01Nn_VJ9i | [
"Harshavardhan Kamarthi",
"Alexander Rodríguez",
"B. Aditya Prakash"
] | Poster | null | For real-time forecasting in domains like public health and macroeconomics, data collection is a non-trivial and demanding task. Often after being initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches a stable value... | [
"Epidemic Forecasting",
"Data revisions",
"Graph Representation learning",
"Time Series Forecasting"
] | null | 4,586 | 2106.04420 | title_snapshot | [
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Approximation and Learning with Deep Convolutional Models: a Kernel Perspective | https://openreview.net/forum?id=lrocYB-0ST2 | [
"Alberto Bietti"
] | Poster | null | The empirical success of deep convolutional networks on tasks involving high-dimensional data such as images or audio suggests that they can efficiently approximate certain functions that are well-suited for such tasks. In this paper, we study this through the lens of kernel methods, by considering simple hierarchical ... | [
"kernel methods",
"deep learning theory",
"convolution",
"approximation",
"generalization"
] | null | 4,570 | 2102.10032 | title_snapshot | [
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Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning | https://openreview.net/forum?id=vgqS1vkkCbE | [
"Dhruv Shah",
"Peng Xu",
"Yao Lu",
"Ted Xiao",
"Alexander T Toshev",
"Sergey Levine",
"brian ichter"
] | Poster | null | Reinforcement learning can train policies that effectively perform complex tasks. However for long-horizon tasks, the performance of these methods degrades with horizon, often necessitating reasoning over and chaining lower-level skills. Hierarchical reinforcement learning aims to enable this by providing a bank of low... | [
"hierarchical reinforcement learning",
"planning",
"representation learning",
"robotics"
] | null | 4,569 | 2111.03189 | title_snapshot | [
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Natural Language Descriptions of Deep Visual Features | https://openreview.net/forum?id=NudBMY-tzDr | [
"Evan Hernandez",
"Sarah Schwettmann",
"David Bau",
"Teona Bagashvili",
"Antonio Torralba",
"Jacob Andreas"
] | Oral | null | Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope,... | [] | null | 4,559 | 2201.11114 | title_snapshot | [
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Learning Hierarchical Structures with Differentiable Nondeterministic Stacks | https://openreview.net/forum?id=5LXw_QplBiF | [
"Brian DuSell",
"David Chiang"
] | Spotlight | null | Learning hierarchical structures in sequential data -- from simple algorithmic patterns to natural language -- in a reliable, generalizable way remains a challenging problem for neural language models. Past work has shown that recurrent neural networks (RNNs) struggle to generalize on held-out algorithmic or syntactic ... | [
"RNN",
"pushdown automata",
"nondeterminism",
"formal languages",
"language modeling"
] | null | 4,554 | 2109.01982 | title_snapshot | [
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Fast Regression for Structured Inputs | https://openreview.net/forum?id=gNp54NxHUPJ | [
"Raphael A Meyer",
"Cameron N Musco",
"Christopher P Musco",
"David Woodruff",
"Samson Zhou"
] | Poster | null | We study the $\ell_p$ regression problem, which requires finding $\mathbf{x}\in\mathbb R^{d}$ that minimizes $\|\mathbf{A}\mathbf{x}-\mathbf{b}\|_p$ for a matrix $\mathbf{A}\in\mathbb R^{n \times d}$ and response vector $\mathbf{b}\in\mathbb R^{n}$. There has been recent interest in developing subsampling methods for t... | [
"regression",
"sublinear time algorithm",
"structured input"
] | null | 4,544 | 2203.07557 | title_snapshot | [
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CrossBeam: Learning to Search in Bottom-Up Program Synthesis | https://openreview.net/forum?id=qhC8mr2LEKq | [
"Kensen Shi",
"Hanjun Dai",
"Kevin Ellis",
"Charles Sutton"
] | Poster | null | Many approaches to program synthesis perform a search within an enormous space of programs to find one that satisfies a given specification. Prior works have used neural models to guide combinatorial search algorithms, but such approaches still explore a huge portion of the search space and quickly become intractable a... | [
"Program Synthesis",
"Bottom-Up Search"
] | null | 4,543 | 2203.10452 | title_snapshot | [
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PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning | https://openreview.net/forum?id=M6M8BEmd6dq | [
"Seng Pei Liew",
"Tsubasa Takahashi",
"Michihiko Ueno"
] | Poster | null | We propose a new framework of synthesizing data using deep generative models in a differentially private manner.
Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion, such that training deep generative models is possible without re-using the original data.
Hence, no ... | [
"Differential Privacy",
"Generative Model"
] | null | 4,542 | 2106.04590 | title_snapshot | [
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Divisive Feature Normalization Improves Image Recognition Performance in AlexNet | https://openreview.net/forum?id=aOX3a9q3RVV | [
"Michelle Miller",
"SueYeon Chung",
"Kenneth D. Miller"
] | Poster | null | Local divisive normalization provides a phenomenological description of many nonlinear response properties of neurons across visual cortical areas. To gain insight into the utility of this operation, we studied the effects on AlexNet of a local divisive normalization between features, with learned parameters. Developin... | [
"divisive normalization",
"AlexNet",
"ImageNet",
"CIFAR-100",
"manifold capacity",
"sparsity",
"receptive fields",
"Batch Normalization",
"Group Normalization",
"Layer Normalization"
] | null | 4,523 | null | null | [
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Evaluating Distributional Distortion in Neural Language Modeling | https://openreview.net/forum?id=bTteFbU99ye | [
"Benjamin LeBrun",
"Alessandro Sordoni",
"Timothy J. O'Donnell"
] | Poster | null | A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a significant amount of the total probability mass of distributions in language (Baayen, 2001). Standard language modeling metrics such as perp... | [] | null | 4,509 | 2203.12788 | title_snapshot | [
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MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining | https://openreview.net/forum?id=r5qumLiYwf9 | [
"Ahmed Imtiaz Humayun",
"Randall Balestriero",
"Richard Baraniuk"
] | Poster | null | Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold, and data distribution on that manifold. However, training samples are often obtained based on preferences, costs, or convenience produ... | [
"Deep Generative Networks",
"Uniform Sampling",
"Fairness",
"Data Augmentation"
] | null | 4,501 | 2110.08009 | title_snapshot | [
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Sampling with Mirrored Stein Operators | https://openreview.net/forum?id=eMudnJsb1T5 | [
"Jiaxin Shi",
"Chang Liu",
"Lester Mackey"
] | Spotlight | null | We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries. Stein Variational Mirror Descent and Mirrored Stein Variational Gradient Descent minimize the Kullback-Leibler (KL) divergence to constrained target distributions by evolving particles in a dual space... | [
"Stein's method",
"Sampling",
"Mirror descent",
"Natural gradient descent",
"Probabilistic inference",
"Bayesian inference",
"Post-selection inference",
"Stein operators"
] | null | 4,500 | 2106.12506 | title_snapshot | [
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Planning in Stochastic Environments with a Learned Model | https://openreview.net/forum?id=X6D9bAHhBQ1 | [
"Ioannis Antonoglou",
"Julian Schrittwieser",
"Sherjil Ozair",
"Thomas K Hubert",
"David Silver"
] | Spotlight | null | Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have instead combined value-equivalent model learning with powerful tree-search methods. This approach is ex... | [
"model-based reinforcement learning",
"deep reinforcement learning",
"tree based search",
"MCTS"
] | null | 4,498 | null | null | [
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Neural Contextual Bandits with Deep Representation and Shallow Exploration | https://openreview.net/forum?id=xnYACQquaGV | [
"Pan Xu",
"Zheng Wen",
"Handong Zhao",
"Quanquan Gu"
] | Poster | null | We study neural contextual bandits, a general class of contextual bandits, where each context-action pair is associated with a raw feature vector, but the specific reward generating function is unknown. We propose a novel learning algorithm that transforms the raw feature vector using the last hidden layer of a deep Re... | [
"neural network",
"deep representation learning"
] | null | 4,495 | 2012.01780 | title_snapshot | [
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PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks | https://openreview.net/forum?id=NoB8YgRuoFU | [
"Siyan Liu",
"Pei Zhang",
"Dan Lu",
"Guannan Zhang"
] | Poster | null | We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI methods require retraining of neural networks (NNs) for every given confidence level and suffer from the crossing issue in calculating multiple P... | [] | null | 4,494 | 2108.02327 | title_snapshot | [
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Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization | https://openreview.net/forum?id=tYRrOdSnVUy | [
"Lixu Wang",
"Shichao Xu",
"Ruiqi Xu",
"Xiao Wang",
"Qi Zhu"
] | Oral | null | As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of protection methods: ownership verification and usage authorization. In this paper, we propose Non-Transferable Learning (NTL), a novel appro... | [
"Domain Adaptation",
"Transfer Learning",
"Societal Considerations of Representation Learning",
"Model Watermark"
] | null | 4,491 | 2106.06916 | title_snapshot | [
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Discriminative Similarity for Data Clustering | https://openreview.net/forum?id=kj0_45Y4r9i | [
"Yingzhen Yang",
"Ping Li"
] | Poster | null | Similarity-based clustering methods separate data into clusters according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose {\em Clustering by Discriminative Similarity (CDS)}, a novel method which learns discriminative similarity for d... | [
"Discriminative Similarity",
"Rademacher Complexity",
"Generalization Bound",
"Data Clustering"
] | null | 4,489 | 2109.08675 | title_snapshot | [
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It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation | https://openreview.net/forum?id=q4tZR1Y-UIs | [
"Yuqing Du",
"Pieter Abbeel",
"Aditya Grover"
] | Poster | null | We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as it requires (a) exploring goals of increasing difficulty, while ensuring that the agent (b) is e... | [
"curriculum generation",
"unsupervised reinforcement learning",
"goal conditioned reinforcement learning",
"multi agent"
] | null | 4,485 | 2202.10608 | title_judge | [
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CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing | https://openreview.net/forum?id=HOjLHrlZhmx | [
"Fan Wu",
"Linyi Li",
"Zijian Huang",
"Yevgeniy Vorobeychik",
"Ding Zhao",
"Bo Li"
] | Poster | null | As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains... | [] | null | 4,469 | 2106.09292 | title_snapshot | [
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Neural Link Prediction with Walk Pooling | https://openreview.net/forum?id=CCu6RcUMwK0 | [
"Liming Pan",
"Cheng Shi",
"Ivan Dokmanić"
] | Poster | null | Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph topology and node attributes. Topology, however, is represented indirectly; state-of-the-art methods based on subgraph classification label nodes with distance to the target link, so that, although topological information is pres... | [
"Graph neural network",
"Link prediction",
"Random walk",
"Graph topology."
] | null | 4,448 | 2110.04375 | title_snapshot | [
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On the Convergence of Certified Robust Training with Interval Bound Propagation | https://openreview.net/forum?id=YeShU5mLfLt | [
"Yihan Wang",
"Zhouxing Shi",
"Quanquan Gu",
"Cho-Jui Hsieh"
] | Poster | null | Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature. In this paper, we present a theoretical anal... | [
"Certified robustness",
"Adversarial robustness",
"Convergence"
] | null | 4,436 | 2203.08961 | title_snapshot | [
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Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators | https://openreview.net/forum?id=sX3XaHwotOg | [
"Yu Meng",
"Chenyan Xiong",
"Payal Bajaj",
"saurabh tiwary",
"Paul N. Bennett",
"Jiawei Han",
"Xia Song"
] | Poster | null | We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-style pretraining, the main encoder is trained as a discriminator to detect replaced tokens generated by auxiliary masked language models (M... | [
"Language Model Pretraining"
] | null | 4,429 | 2204.03243 | title_snapshot | [
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Neural Structured Prediction for Inductive Node Classification | https://openreview.net/forum?id=YWNAX0caEjI | [
"Meng Qu",
"Huiyu Cai",
"Jian Tang"
] | Oral | null | This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. This problem has been extensively studied with graph neural networks (GNNs) by learning effective node representations, as well as tr... | [] | null | 4,412 | 2204.07524 | title_snapshot | [
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Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations | https://openreview.net/forum?id=0jP2n0YFmKG | [
"Anuroop Sriram",
"Abhishek Das",
"Brandon M Wood",
"Siddharth Goyal",
"C. Lawrence Zitnick"
] | Poster | null | Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory inten... | [
"Graph Neural Networks",
"Atomic Simulations",
"Computational Chemistry"
] | null | 4,408 | 2203.09697 | title_snapshot | [
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RotoGrad: Gradient Homogenization in Multitask Learning | https://openreview.net/forum?id=T8wHz4rnuGL | [
"Adrián Javaloy",
"Isabel Valera"
] | Spotlight | null | Multitask learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negative transfer. Previous works have tracked down this issue to the disparities in gradient magnitudes a... | [
"multitask learning",
"conflicting gradients",
"negative transfer"
] | null | 4,402 | 2103.02631 | title_snapshot | [
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On Improving Adversarial Transferability of Vision Transformers | https://openreview.net/forum?id=D6nH3719vZy | [
"Muzammal Naseer",
"Kanchana Ranasinghe",
"Salman Khan",
"Fahad Khan",
"Fatih Porikli"
] | Spotlight | null | Vision transformers (ViTs) process input images as sequences of patches via self-attention; a radically different architecture than convolutional neural networks (CNNs). This makes it interesting to study the adversarial feature space of ViT models and their transferability. In particular, we observe that adversarial ... | [
"Vision Transformers",
"Adversarial Perturbations"
] | null | 4,395 | 2106.04169 | title_snapshot | [
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On Predicting Generalization using GANs | https://openreview.net/forum?id=eW5R4Cek6y6 | [
"Yi Zhang",
"Arushi Gupta",
"Nikunj Saunshi",
"Sanjeev Arora"
] | Spotlight | null | Research on generalization bounds for deep networks seeks to give ways to predict test error using just the training dataset and the network parameters. While generalization bounds can give many insights about architecture design, training algorithms etc., what they do not currently do is yield good predictions for act... | [
"generalization",
"generative adversarial network"
] | null | 4,393 | 2111.14212 | title_snapshot | [
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Understanding and Leveraging Overparameterization in Recursive Value Estimation | https://openreview.net/forum?id=shbAgEsk3qM | [
"Chenjun Xiao",
"Bo Dai",
"Jincheng Mei",
"Oscar A Ramirez",
"Ramki Gummadi",
"Chris Harris",
"Dale Schuurmans"
] | Poster | null | The theory of function approximation in reinforcement learning (RL) typically considers low capacity representations that incur a tradeoff between approximation error, stability and generalization. Current deep architectures, however, operate in an overparameterized regime where approximation error is not necessarily ... | [
"Temporal Difference Learning",
"Residual Minimization",
"Value Estimation",
"Overparameterization"
] | null | 4,376 | null | null | [
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Optimization and Adaptive Generalization of Three layer Neural Networks | https://openreview.net/forum?id=dPyRNUlttBv | [
"Khashayar Gatmiry",
"Stefanie Jegelka",
"Jonathan Kelner"
] | Poster | null | While there has been substantial recent work studying generalization of neural networks,
the ability of deep nets in automating the process of feature extraction still evades a thorough mathematical understanding.
As a step toward this goal, we analyze learning and generalization of a three-layer neural network wit... | [
"deep learning theory",
"adaptive kernel",
"robust deep learning",
"neural tangent kernel",
"adaptive generalization",
"non-convex optimization"
] | null | 4,368 | null | null | [
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Non-Parallel Text Style Transfer with Self-Parallel Supervision | https://openreview.net/forum?id=-TSe5o7STVR | [
"Ruibo Liu",
"Chongyang Gao",
"Chenyan Jia",
"Guangxuan Xu",
"Soroush Vosoughi"
] | Poster | null | The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentenc... | [
"style transfer",
"non-parallel corpus",
"imitation learning",
"language models",
"political stance transfer"
] | null | 4,365 | 2204.08123 | title_snapshot | [
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Can an Image Classifier Suffice For Action Recognition? | https://openreview.net/forum?id=qhkFX-HLuHV | [
"Quanfu Fan",
"Chun-Fu Chen",
"Rameswar Panda"
] | Poster | null | We explore a new perspective on video understanding by casting the video recognition problem as an image recognition task. Our approach rearranges input video frames into super images, which allow for training an image classifier directly to fulfill the task of action recognition, in exactly the same way as image class... | [
"action recognition",
"image classifier",
"super image",
"vision transformer"
] | null | 4,364 | 2106.14104 | title_snapshot | [
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On the Connection between Local Attention and Dynamic Depth-wise Convolution | https://openreview.net/forum?id=L3_SsSNMmy | [
"Qi Han",
"Zejia Fan",
"Qi Dai",
"Lei Sun",
"Ming-Ming Cheng",
"Jiaying Liu",
"Jingdong Wang"
] | Spotlight | null | Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in Local Vision Transformer, local attention, performs the attention separately over small local windows. We rephrase local attention as a chann... | [
"local attention",
"depth-wise convolution",
"dynamic depth-wise convolution",
"weight sharing",
"dynamic weight"
] | null | 4,356 | 2106.04263 | title_snapshot | [
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Strength of Minibatch Noise in SGD | https://openreview.net/forum?id=uorVGbWV5sw | [
"Liu Ziyin",
"Kangqiao Liu",
"Takashi Mori",
"Masahito Ueda"
] | Spotlight | null | The noise in stochastic gradient descent (SGD), caused by minibatch sampling, is poorly understood despite its practical importance in deep learning. This work presents the first systematic study of the SGD noise and fluctuations close to a local minimum. We first analyze the SGD noise in linear regression in detail an... | [
"stochastic gradient descent",
"minibatch noise",
"discrete-time SGD",
"noise and fluctuation",
"exact solvable models"
] | null | 4,355 | 2102.05375 | title_snapshot | [
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Learning more skills through optimistic exploration | https://openreview.net/forum?id=cU8rknuhxc | [
"DJ Strouse",
"Kate Baumli",
"David Warde-Farley",
"Volodymyr Mnih",
"Steven Stenberg Hansen"
] | Spotlight | null | Unsupervised skill learning objectives (Eysenbach et al., 2019; Gregor et al., 2016) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate distingui... | [
"intrinsic control",
"skill discovery",
"unsupervised skill learning",
"uncertainty estimation",
"optimistic exploration",
"variational information maximization"
] | null | 4,352 | 2107.14226 | title_snapshot | [
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Interacting Contour Stochastic Gradient Langevin Dynamics | https://openreview.net/forum?id=IK9ap6nxXr2 | [
"Wei Deng",
"Siqi Liang",
"Botao Hao",
"Guang Lin",
"Faming Liang"
] | Poster | null | We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equival... | [
"stochastic gradient Langevin dynamics",
"MCMC",
"importance sampling",
"Wang-Landau algorithm",
"Parallel MCMC Methods",
"stochastic approximation"
] | null | 4,326 | 2202.09867 | title_snapshot | [
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NeuPL: Neural Population Learning | https://openreview.net/forum?id=MIX3fJkl_1 | [
"Siqi Liu",
"Luke Marris",
"Daniel Hennes",
"Josh Merel",
"Nicolas Heess",
"Thore Graepel"
] | Poster | null | Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from two issues in real-world games: a) under finite bu... | [
"Multi-Agent Learning",
"Game Theory",
"Population Learning"
] | null | 4,325 | 2202.07415 | title_snapshot | [
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Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory | https://openreview.net/forum?id=PLDOnFoVm4 | [
"Zhi Zhang",
"Zhuoran Yang",
"Han Liu",
"Pratap Tokekar",
"Furong Huang"
] | Spotlight | null | We study reinforcement learning for partially observable multi-agent systems where each agent only has access to its own observation and reward and aims to maximize its cumulative rewards. To handle partial observations, we propose graph-assisted predictive state representations (GAPSR), a scalable multi-agent represen... | [
"Multi-agent Reinforcement Learning",
"Predictive State Representation",
"Dynamic Interaction Graph"
] | null | 4,323 | null | null | [
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A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?" | https://openreview.net/forum?id=uxgg9o7bI_3 | [
"Asiri Wijesinghe",
"Qing Wang"
] | Oral | null | We propose a new perspective on designing powerful Graph Neural Networks (GNNs). In a nutshell, this enables a general solution to inject structural properties of graphs into a message-passing aggregation scheme of GNNs. As a theoretical basis, we develop a new hierarchy of local isomorphism on neighborhood subgraphs. ... | [
"Graph Neural Networks",
"Graph Isomorphism",
"Weisfeiler Lehman"
] | null | 4,320 | null | null | [
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DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator | https://openreview.net/forum?id=hniLRD_XCA | [
"Minghao Han",
"Jacob Euler-Rolle",
"Robert K. Katzschmann"
] | Poster | null | The Koopman operator theory linearly describes nonlinear dynamical systems in a high-dimensional functional space and it allows to apply linear control methods to highly nonlinear systems. However, the Koopman operator does not account for any uncertainty in dynamical systems, causing it to perform poorly in real-world... | [
"Koopman Operator",
"Robust Control",
"Robotics",
"Model Predictive Control",
"Soft Robotics"
] | null | 4,319 | null | null | [
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Neural Network Approximation based on Hausdorff distance of Tropical Zonotopes | https://openreview.net/forum?id=oiZJwC_fyS | [
"Panagiotis Misiakos",
"Georgios Smyrnis",
"George Retsinas",
"Petros Maragos"
] | Poster | null | In this work we theoretically contribute to neural network approximation by providing a novel tropical geometrical viewpoint to structured neural network compression. In particular, we show that the approximation error between two neural networks with ReLU activations and one hidden layer depends on the Hausdorff dista... | [
"Tropical Geometry",
"Zonotopes",
"Hausdorff Approximation",
"Neural Network Compression"
] | null | 4,300 | null | null | [
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Learning Towards The Largest Margins | https://openreview.net/forum?id=hqkhcFHOeKD | [
"Xiong Zhou",
"Xianming Liu",
"Deming Zhai",
"Junjun Jiang",
"Xin Gao",
"Xiangyang Ji"
] | Poster | null | One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. The classical softmax loss does not explicitly encourage discriminative learning of features. A popular direction of research is to incorporat... | [
"loss function design",
"margin-based loss",
"classification"
] | null | 4,295 | 2206.11589 | title_snapshot | [
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Adversarial Support Alignment | https://openreview.net/forum?id=26gKg6x-ie | [
"Shangyuan Tong",
"Timur Garipov",
"Yang Zhang",
"Shiyu Chang",
"Tommi S. Jaakkola"
] | Spotlight | null | We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matched. We propose symmetric support difference as a divergence measure to quantify the mismatch between supports. We show that select discrimi... | [
"support alignment",
"distribution alignment",
"optimal transport",
"domain adaptation"
] | null | 4,292 | 2203.08908 | title_snapshot | [
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Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations? | https://openreview.net/forum?id=28ib9tf6zhr | [
"Yonggan Fu",
"Shunyao Zhang",
"Shang Wu",
"Cheng Wan",
"Yingyan Lin"
] | Poster | null | Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfill the goal of deploying ViTs into real-world vision applications, their robustness against potential malicious attacks has gained increasin... | [
"Vision transformer",
"adversarial examples",
"robustness"
] | null | 4,289 | 2203.08392 | title_snapshot | [
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AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation | https://openreview.net/forum?id=Q5uh1Nvv5dm | [
"David Berthelot",
"Rebecca Roelofs",
"Kihyuk Sohn",
"Nicholas Carlini",
"Alexey Kurakin"
] | Poster | null | We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a unified solution for unsupervised domain adaptation (UDA), semi-supervised learning ... | [
"unsupervised domain adaptation",
"semi-supervised learning",
"semi-supervised domain adaptation"
] | null | 4,287 | 2106.04732 | title_snapshot | [
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Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound | https://openreview.net/forum?id=l_amHf1oaK | [
"Claudio Ferrari",
"Mark Niklas Mueller",
"Nikola Jovanović",
"Martin Vechev"
] | Poster | null | State-of-the-art neural network verifiers are fundamentally based on one of two paradigms: either encoding the whole verification problem via tight multi-neuron convex relaxations or applying a Branch-and-Bound (BaB) procedure leveraging imprecise but fast bounding methods on a large number of easier subproblems. The f... | [
"Certified Robustness",
"Branch-and-Bound",
"Convex Relaxation"
] | null | 4,286 | 2205.00263 | title_snapshot | [
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GreaseLM: Graph REASoning Enhanced Language Models | https://openreview.net/forum?id=41e9o6cQPj | [
"Xikun Zhang",
"Antoine Bosselut",
"Michihiro Yasunaga",
"Hongyu Ren",
"Percy Liang",
"Christopher D Manning",
"Jure Leskovec"
] | Spotlight | null | Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it. However, pretrained language models (LM), the foundation of most modern QA systems, do not robustly represent latent relationships between concepts, which is necessary for reasonin... | [
"language models",
"commonsense",
"question answering",
"knowledge graphs",
"KG augmentation"
] | null | 4,280 | null | null | [
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Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality | https://openreview.net/forum?id=VFBjuF8HEp | [
"Daniel Watson",
"William Chan",
"Jonathan Ho",
"Mohammad Norouzi"
] | Poster | null | Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models typically require hundreds of forward passes through the model to generate a single high-fidelity sample. We introduce Differentiable Diffu... | [] | null | 4,276 | 2202.05830 | title_snapshot | [
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Distribution Compression in Near-Linear Time | https://openreview.net/forum?id=lzupY5zjaU9 | [
"Abhishek Shetty",
"Raaz Dwivedi",
"Lester Mackey"
] | Poster | null | In distribution compression, one aims to accurately summarize a probability distribution $\mathbb{P}$ using a small number of representative points. Near-optimal thinning procedures achieve this goal by sampling $n$ points from a Markov chain and identifying $\sqrt{n}$ points with $\widetilde{\mathcal{O}}(1/\sqrt{n})$ ... | [
"Distribution compression",
"linear time",
"thinning",
"i.i.d. sampling",
"Markov chain Monte Carlo",
"maximum mean discrepancy",
"reproducing kernel Hilbert space"
] | null | 4,274 | 2111.07941 | title_snapshot | [
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Capturing Structural Locality in Non-parametric Language Models | https://openreview.net/forum?id=nnU3IUMJmN | [
"Frank F. Xu",
"Junxian He",
"Graham Neubig",
"Vincent Josua Hellendoorn"
] | Poster | null | Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or project hierarchies in source code repositories. In this paper, we explore utilizing this structural locality within non-parametric language mod... | [] | null | 4,271 | 2110.02870 | title_snapshot | [
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Audio Lottery: Speech Recognition Made Ultra-Lightweight, Noise-Robust, and Transferable | https://openreview.net/forum?id=9Nk6AJkVYB | [
"Shaojin Ding",
"Tianlong Chen",
"Zhangyang Wang"
] | Poster | null | Lightweight speech recognition models have seen explosive demands owing to a growing amount of speech-interactive features on mobile devices. Since designing such systems from scratch is non-trivial, practitioners typically choose to compress large (pre-trained) speech models. Recently, lottery ticket hypothesis reveal... | [
"Speech Recognition",
"Lottery Ticket Hypothesis"
] | null | 4,269 | null | null | [
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Learning meta-features for AutoML | https://openreview.net/forum?id=DTkEfj0Ygb8 | [
"Herilalaina Rakotoarison",
"Louisot Milijaona",
"Andry RASOANAIVO",
"Michele Sebag",
"Marc Schoenauer"
] | Spotlight | null | This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed \mf s with the space of distribut... | [
"AutoML",
"Meta-features",
"Hyper-parameter Optimization",
"Optimal Transport"
] | null | 4,263 | null | null | [
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Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | https://openreview.net/forum?id=LdlwbBP2mlq | [
"Chulhee Yun",
"Shashank Rajput",
"Suvrit Sra"
] | Oral | null | In distributed learning, local SGD (also known as federated averaging) and its simple baseline minibatch SGD are widely studied optimization methods. Most existing analyses of these methods assume independent and unbiased gradient estimates obtained via with-replacement sampling. In contrast, we study shuffling-based v... | [
"Local SGD",
"Minibatch SGD",
"Shuffling",
"Without-replacement",
"Convex Optimization",
"Stochastic Optimization",
"Federated Learning",
"Large Scale Learning",
"Distributed Learning"
] | null | 4,253 | 2110.10342 | title_snapshot | [
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Learning to Map for Active Semantic Goal Navigation | https://openreview.net/forum?id=swrMQttr6wN | [
"Georgios Georgakis",
"Bernadette Bucher",
"Karl Schmeckpeper",
"Siddharth Singh",
"Kostas Daniilidis"
] | Poster | null | We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments. Current methods learn to implicitly encode these priors through goal-oriented navigat... | [
"visual navigation",
"semantic map",
"uncertainty estimation"
] | null | 4,251 | 2106.15648 | title_snapshot | [
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Benchmarking the Spectrum of Agent Capabilities | https://openreview.net/forum?id=1W0z96MFEoH | [
"Danijar Hafner"
] | Poster | null | Evaluating the general abilities of intelligent agents requires complex simulation environments. Existing benchmarks typically evaluate only one narrow task per environment, requiring researchers to perform expensive training runs on many different environments. We introduce Crafter, an open world survival game with vi... | [
"Evaluation",
"Reinforcement Learning",
"Environment",
"Benchmark",
"Unsupervised Reinforcement Learning",
"Exploration"
] | null | 4,249 | 2109.06780 | title_snapshot | [
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Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks | https://openreview.net/forum?id=vqGi8Kp0wM | [
"Peihao Zhu",
"Rameen Abdal",
"John Femiani",
"Peter Wonka"
] | Poster | null | We present a new method for one shot domain adaptation. The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method com... | [
"GAN",
"StyleGAN",
"Clip",
"Domain Adaptation",
"Style Transfer",
"Single Shot"
] | null | 4,248 | 2110.08398 | title_snapshot | [
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The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions | https://openreview.net/forum?id=Z7Lk2cQEG8a | [
"Yifei Wang",
"Jonathan Lacotte",
"Mert Pilanci"
] | Oral | null | We prove that finding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, characterizes all optimal solutions, and does not leverage duality-based analysis which was recently used to lift neural network training into... | [
"Neural networks",
"global optimization",
"convex optimization",
"convex analysis"
] | null | 4,247 | 2006.05900 | title_judge | [
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On Evaluation Metrics for Graph Generative Models | https://openreview.net/forum?id=EnwCZixjSh | [
"Rylee Thompson",
"Boris Knyazev",
"Elahe Ghalebi",
"Jungtaek Kim",
"Graham W. Taylor"
] | Poster | null | In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does no... | [] | null | 4,244 | 2201.09871 | title_snapshot | [
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Selective Ensembles for Consistent Predictions | https://openreview.net/forum?id=HfUyCRBeQc | [
"Emily Black",
"Klas Leino",
"Matt Fredrikson"
] | Poster | null | Recent work has shown that models trained to the same objective, and which achieve similar measures of accuracy on consistent test data, may nonetheless behave very differently on individual predictions. This inconsistency is undesirable in high-stakes contexts, such as medical diagnosis and finance. We show that this ... | [
"consistency",
"prediction consistency",
"model duplicity",
"inconsistent predictions",
"deep models",
"deep networks",
"explanations",
"saliency maps",
"gradient-based explanations",
"fairness",
"interpretability"
] | null | 4,240 | 2111.08230 | title_snapshot | [
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Graph Condensation for Graph Neural Networks | https://openreview.net/forum?id=WLEx3Jo4QaB | [
"Wei Jin",
"Lingxiao Zhao",
"Shichang Zhang",
"Yozen Liu",
"Jiliang Tang",
"Neil Shah"
] | Poster | null | Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, origina... | [
"data-efficient learning",
"graph generation",
"graph neural networks"
] | null | 4,239 | 2110.07580 | title_snapshot | [
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DIVA: Dataset Derivative of a Learning Task | https://openreview.net/forum?id=bVvMOtLMiw | [
"Yonatan Dukler",
"Alessandro Achille",
"Giovanni Paolini",
"Avinash Ravichandran",
"Marzia Polito",
"Stefano Soatto"
] | Poster | null | We present a method to compute the derivative of a learning task with respect to a dataset. A learning task is a function from a training set to the validation error, which can be represented by a trained deep neural network (DNN). The ``dataset derivative'' is a linear operator, computed around the trained model, that... | [
"Leave one out cross validation",
"AutoML",
"dataset optimization"
] | null | 4,238 | 2111.09785 | title_snapshot | [
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Towards General Function Approximation in Zero-Sum Markov Games | https://openreview.net/forum?id=sA4qIu3zv6v | [
"Baihe Huang",
"Jason D. Lee",
"Zhaoran Wang",
"Zhuoran Yang"
] | Poster | null | This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value
function or the model is parameterized by general function classes. Provably efficient
algorithms for both decoupled and coordinated settings are developed. In the ... | [] | null | 4,231 | 2107.14702 | title_snapshot | [
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Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings | https://openreview.net/forum?id=6PvWo1kEvlT | [
"Kartik Goyal",
"Chris Dyer",
"Taylor Berg-Kirkpatrick"
] | Poster | null | While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences, it is still an open question if these MLMs specify a principled probability distribution over the space of possible sequences. In this paper... | [
"Masked Language Models",
"Energy-based models",
"Metropolis Hastings Monte Carlo",
"Bidirectional Sequence models"
] | null | 4,229 | 2106.02736 | title_snapshot | [
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ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods | https://openreview.net/forum?id=EZNOb_uNpJk | [
"Victor Schmidt",
"Alexandra Luccioni",
"Mélisande Teng",
"Tianyu Zhang",
"Alexia Reynaud",
"Sunand Raghupathi",
"Gautier Cosne",
"Adrien Juraver",
"Vahe Vardanyan",
"Alex Hernández-García",
"Yoshua Bengio"
] | Poster | null | Climate change is a major threat to humanity and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding its seemingly abstract and distant consequences. Projecting the potential impacts of extreme climat... | [
"GAN",
"Climate Change",
"Domain Adaptation",
"Representation Learning",
"Computer Vision",
"Application"
] | null | 4,226 | 2110.02871 | title_snapshot | [
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0.011... |
A Comparison of Hamming Errors of Representative Variable Selection Methods | https://openreview.net/forum?id=nhN-fqxmNGx | [
"Tracy Ke",
"Longlin Wang"
] | Poster | null | Lasso is a celebrated method for variable selection in linear models, but it faces challenges when the covariates are moderately or strongly correlated. This motivates alternative approaches such as using a non-convex penalty, adding a ridge regularization, or conducting a post-Lasso thresholding. In this paper, we com... | [
"Lasso",
"Hamming error",
"phase diagram",
"rare and weak signals",
"elastic net",
"SCAD",
"thresholded Lasso",
"forward selection",
"forward backward selection"
] | null | 4,224 | 2203.15075 | title_snapshot | [
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Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction | https://openreview.net/forum?id=Dup_dDqkZC5 | [
"Roger Girgis",
"Florian Golemo",
"Felipe Codevilla",
"Martin Weiss",
"Jim Aldon D'Souza",
"Samira Ebrahimi Kahou",
"Felix Heide",
"Christopher Pal"
] | Spotlight | null | Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a representation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transforme... | [
"trajectory prediction",
"motion forecasting",
"transformers",
"latent variable models"
] | null | 4,221 | 2104.00563 | title_snapshot | [
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A Program to Build E(N)-Equivariant Steerable CNNs | https://openreview.net/forum?id=WE4qe9xlnQw | [
"Gabriele Cesa",
"Leon Lang",
"Maurice Weiler"
] | Poster | null | Equivariance is becoming an increasingly popular design choice to build data efficient neural networks by exploiting prior knowledge about the symmetries of the problem at hand. Euclidean steerable CNNs are one of the most common classes of equivariant networks. While the constraints these architectures need to satisfy... | [
"equivariance",
"3D",
"geometric deep learning",
"isometries",
"steerable CNN"
] | null | 4,218 | null | null | [
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Minimax Optimization with Smooth Algorithmic Adversaries | https://openreview.net/forum?id=UdxJ2fJx7N0 | [
"Tanner Fiez",
"Chi Jin",
"Praneeth Netrapalli",
"Lillian J Ratliff"
] | Poster | null | This paper considers minimax optimization $\min_x \max_y f(x, y)$ in the challenging setting where $f$ can be both nonconvex in $x$ and nonconcave in $y$. Though such optimization problems arise in many machine learning paradigms including training generative adversarial networks (GANs) and adversarially robust models,... | [
"Minimax optimization",
"two player zero sum games",
"generative adversarial networks",
"adversarial training"
] | null | 4,217 | 2106.01488 | title_snapshot | [
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Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics | https://openreview.net/forum?id=RQLLzMCefQu | [
"Yonathan Efroni",
"Dipendra Misra",
"Akshay Krishnamurthy",
"Alekh Agarwal",
"John Langford"
] | Oral | null | Many real-world applications of reinforcement learning (RL) require the agent to deal with high-dimensional observations such as those generated from a megapixel camera. Prior work has addressed such problems with representation learning, through which the agent can provably extract endogenous, latent state information... | [
"Reinforcement Learning Theory",
"Invariant Representation",
"Rich Observation Reinforcement Learning",
"Exogenous Noise",
"Inverse Dynamics"
] | null | 4,212 | 2110.08847 | title_judge | [
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On Distributed Adaptive Optimization with Gradient Compression | https://openreview.net/forum?id=CI-xXX9dg9l | [
"Xiaoyun Li",
"Belhal Karimi",
"Ping Li"
] | Poster | null | We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averagin... | [] | null | 4,202 | 2205.05632 | title_snapshot | [
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Leveraging unlabeled data to predict out-of-distribution performance | https://openreview.net/forum?id=o_HsiMPYh_x | [
"Saurabh Garg",
"Sivaraman Balakrishnan",
"Zachary Chase Lipton",
"Behnam Neyshabur",
"Hanie Sedghi"
] | Poster | null | Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions
that may cause performance drops. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Aver... | [
"Distribution Shift",
"OOD error prediction",
"Deep Learning"
] | null | 4,190 | 2201.04234 | title_snapshot | [
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VC dimension of partially quantized neural networks in the overparametrized regime | https://openreview.net/forum?id=7udZAsEzd60 | [
"Yutong Wang",
"Clayton Scott"
] | Poster | null | Vapnik-Chervonenkis (VC) theory has so far been unable to explain the small generalization error of overparametrized neural networks. Indeed, existing applications of VC theory to large networks obtain upper bounds on VC dimension that are proportional to the number of weights, and for a large class of networks, these ... | [
"VC dimension",
"quantized neural networks",
"classification",
"minimax theory",
"overparametrization"
] | null | 4,186 | 2110.02456 | title_snapshot | [
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Optimal Representations for Covariate Shift | https://openreview.net/forum?id=Rf58LPCwJj0 | [
"Yangjun Ruan",
"Yann Dubois",
"Chris J. Maddison"
] | Poster | null | Machine learning systems often experience a distribution shift between training and testing. In this paper, we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predicto... | [
"distribution shift",
"domain generalization",
"representation learning",
"self-supervised learning",
"invariance",
"robustness"
] | null | 4,179 | 2201.00057 | title_snapshot | [
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... |
Fortuitous Forgetting in Connectionist Networks | https://openreview.net/forum?id=ei3SY1_zYsE | [
"Hattie Zhou",
"Ankit Vani",
"Hugo Larochelle",
"Aaron Courville"
] | Poster | null | Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce forget-and-relearn as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting... | [
"Neural Networks",
"Generalization",
"Iterative Training",
"Compositionality",
"Iterated Learning"
] | null | 4,178 | 2202.00155 | title_snapshot | [
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Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective | https://openreview.net/forum?id=5FUq05QRc5b | [
"Qi Lyu",
"Xiao Fu",
"Weiran Wang",
"Songtao Lu"
] | Spotlight | null | Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA]... | [] | null | 4,176 | 2106.07115 | title_snapshot | [
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EigenGame Unloaded: When playing games is better than optimizing | https://openreview.net/forum?id=So6YAqnqgMj | [
"Ian Gemp",
"Brian McWilliams",
"Claire Vernade",
"Thore Graepel"
] | Poster | null | We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGame's updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting. In this work, we propose an unbiased stochastic update that is as... | [
"pca",
"principal components analysis",
"nash",
"games",
"eigendecomposition",
"svd",
"singular value decomposition"
] | null | 4,175 | 2102.04152 | title_snapshot | [
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-0... |
Contextualized Scene Imagination for Generative Commonsense Reasoning | https://openreview.net/forum?id=Oh1r2wApbPv | [
"PeiFeng Wang",
"Jonathan Zamora",
"Junfeng Liu",
"Filip Ilievski",
"Muhao Chen",
"Xiang Ren"
] | Poster | null | Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) skills are lacking in state-of-the-art text generation methods. Descriptive sentences about arbitrary concepts generated by neural text gener... | [
"Commonsense reasoning",
"constrained text generation",
"knowledge representation"
] | null | 4,167 | 2112.06318 | title_snapshot | [
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0.013016085140407085,
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-0.05537872016429901,
0.002... |
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents | https://openreview.net/forum?id=Wm3EA5OlHsG | [
"Jiquan Ngiam",
"Vijay Vasudevan",
"Benjamin Caine",
"Zhengdong Zhang",
"Hao-Tien Lewis Chiang",
"Jeffrey Ling",
"Rebecca Roelofs",
"Alex Bewley",
"Chenxi Liu",
"Ashish Venugopal",
"David J Weiss",
"Benjamin Sapp",
"Zhifeng Chen",
"Jonathon Shlens"
] | Poster | null | Predicting the motion of multiple agents is necessary for planning in dynamic environments. This task is challenging for autonomous driving since agents (e.g., vehicles and pedestrians) and their associated behaviors may be diverse and influence one another. Most prior work have focused on predicting independent future... | [
"trajectory prediction",
"motion forecasting",
"multi-task learning",
"attention",
"autonomous vehicles"
] | null | 4,165 | 2106.08417 | title_judge | [
-0.013004750944674015,
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-0.03162037581205368,
-0.02828560583293438,
0.020230302587151527,
-0.08159495145082474,
-0.... |
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals | https://openreview.net/forum?id=qY79G8jGsep | [
"Asma Ghandeharioun",
"Been Kim",
"Chun-Liang Li",
"Brendan Jou",
"Brian Eoff",
"Rosalind Picard"
] | Poster | null | Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal benefits of counterfactual explanations is allowing users to explore "what-if" scenarios through what... | [
"Explainability",
"Interpretability",
"Counterfactual generation",
"Generative Adversarial Network",
"Variational Autoencoder"
] | null | 4,164 | 2105.15164 | title_snapshot | [
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-0.0343901626765728,
-0.03818608075380325,
0.017767284065485,
-0.05713758245110512,
0.03443... |
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