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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
l0mSUROpwY | Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures | https://openreview.net/forum?id=l0mSUROpwY | [
"Pedro Hermosilla",
"Marco Schäfer",
"Matej Lang",
"Gloria Fackelmann",
"Pere-Pau Vázquez",
"Barbora Kozlikova",
"Michael Krone",
"Tobias Ritschel",
"Timo Ropinski"
] | Poster | null | Proteins perform a large variety of functions in living organisms and thus play a key role in biology. However, commonly used algorithms in protein representation learning were not specifically designed for protein data, and are therefore not able to capture all relevant structural levels of a protein during learning. ... | [
"classification",
"bioinformatics"
] | null | 1,870 | 2007.06252 | title_snapshot |
45uOPa46Kh | Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule | https://openreview.net/forum?id=45uOPa46Kh | [
"Shuhei Kurita",
"Kyunghyun Cho"
] | Poster | null | Vision-and-language navigation (VLN) is a task in which an agent is embodied in a realistic 3D environment and follows an instruction to reach the goal node. While most of the previous studies have built and investigated a discriminative approach, we notice that there are in fact two possible approaches to building suc... | [
"vision-and-language-navigation"
] | null | 1,868 | 2009.07783 | title_snapshot |
90JprVrJBO | Learning a Latent Search Space for Routing Problems using Variational Autoencoders | https://openreview.net/forum?id=90JprVrJBO | [
"André Hottung",
"Bhanu Bhandari",
"Kevin Tierney"
] | Poster | null | Methods for automatically learning to solve routing problems are rapidly improving in performance. While most of these methods excel at generating solutions quickly, they are unable to effectively utilize longer run times because they lack a sophisticated search component. We present a learning-based optimization appro... | [
"heuristic search",
"variational autoencoders",
"learning to optimize",
"routing problems",
"traveling salesperson problem",
"vehicle routing problem",
"combinatorial optimization"
] | null | 1,866 | null | null |
0oabwyZbOu | Mastering Atari with Discrete World Models | https://openreview.net/forum?id=0oabwyZbOu | [
"Danijar Hafner",
"Timothy P Lillicrap",
"Mohammad Norouzi",
"Jimmy Ba"
] | Poster | null | Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasible for some tasks, m... | [
"Atari",
"world models",
"model-based reinforcement learning",
"reinforcement learning",
"planning",
"actor critic"
] | null | 1,860 | 2010.02193 | title_snapshot |
I4c4K9vBNny | Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling | https://openreview.net/forum?id=I4c4K9vBNny | [
"Đorđe Miladinović",
"Aleksandar Stanić",
"Stefan Bauer",
"Jürgen Schmidhuber",
"Joachim M. Buhmann"
] | Poster | null | How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net a... | [
"Neural networks",
"Deep generative models",
"Image Modeling",
"Variational Autoencoders"
] | null | 1,859 | 2103.08877 | title_snapshot |
uR9LaO_QxF | Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation | https://openreview.net/forum?id=uR9LaO_QxF | [
"Emilio Parisotto",
"Russ Salakhutdinov"
] | Poster | null | Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on un-accelerated hardware such as CPUs, which likewise restricts model size to prevent int... | [
"Deep Reinforcement Learning",
"Memory",
"Transformers",
"Distillation"
] | null | 1,854 | 2104.01655 | title_snapshot |
ipUPfYxWZvM | IOT: Instance-wise Layer Reordering for Transformer Structures | https://openreview.net/forum?id=ipUPfYxWZvM | [
"Jinhua Zhu",
"Lijun Wu",
"Yingce Xia",
"Shufang Xie",
"Tao Qin",
"Wengang Zhou",
"Houqiang Li",
"Tie-Yan Liu"
] | Poster | null | With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the \emph{layer order} is fixed and kept the same across da... | [
"Layer order",
"Transformers",
"Instance-wise Learning"
] | null | 1,853 | 2103.03457 | title_snapshot |
GFsU8a0sGB | Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | https://openreview.net/forum?id=GFsU8a0sGB | [
"Maruan Al-Shedivat",
"Jennifer Gillenwater",
"Eric Xing",
"Afshin Rostamizadeh"
] | Poster | null | Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learni... | [
"federated learning",
"posterior inference",
"MCMC"
] | null | 1,852 | 2010.05273 | title_snapshot |
XSLF1XFq5h | Getting a CLUE: A Method for Explaining Uncertainty Estimates | https://openreview.net/forum?id=XSLF1XFq5h | [
"Javier Antoran",
"Umang Bhatt",
"Tameem Adel",
"Adrian Weller",
"José Miguel Hernández-Lobato"
] | Oral | null | Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bay... | [
"interpretability",
"uncertainty",
"explainability"
] | null | 1,848 | 2006.06848 | title_snapshot |
Nc3TJqbcl3 | Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers | https://openreview.net/forum?id=Nc3TJqbcl3 | [
"Cristina Pinneri",
"Shambhuraj Sawant",
"Sebastian Blaes",
"Georg Martius"
] | Poster | null | Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance and are considered state-of-the-art in the model-based reinforcement learning community. However, this... | [
"reinforcement learning",
"zero-order optimization",
"policy learning",
"model-based learning",
"robotics",
"model predictive control"
] | null | 1,844 | null | null |
6Tm1mposlrM | Sharpness-aware Minimization for Efficiently Improving Generalization | https://openreview.net/forum?id=6Tm1mposlrM | [
"Pierre Foret",
"Ariel Kleiner",
"Hossein Mobahi",
"Behnam Neyshabur"
] | Spotlight | null | In today's heavily overparameterized models, the value of the training loss provides few guarantees on model generalization ability. Indeed, optimizing only the training loss value, as is commonly done, can easily lead to suboptimal model quality. Motivated by the connection between geometry of the loss landscape and g... | [
"Sharpness Minimization",
"Generalization",
"Regularization",
"Training Method",
"Deep Learning"
] | null | 1,839 | 2010.01412 | title_snapshot |
mQPBmvyAuk | BREEDS: Benchmarks for Subpopulation Shift | https://openreview.net/forum?id=mQPBmvyAuk | [
"Shibani Santurkar",
"Dimitris Tsipras",
"Aleksander Madry"
] | Poster | null | We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise... | [
"benchmarks",
"distribution shift",
"hierarchy",
"robustness"
] | null | 1,836 | 2008.04859 | title_snapshot |
NQbnPjPYaG6 | On the Impossibility of Global Convergence in Multi-Loss Optimization | https://openreview.net/forum?id=NQbnPjPYaG6 | [
"Alistair Letcher"
] | Poster | null | Under mild regularity conditions, gradient-based methods converge globally to a critical point in the single-loss setting. This is known to break down for vanilla gradient descent when moving to multi-loss optimization, but can we hope to build some algorithm with global guarantees? We negatively resolve this open prob... | [
"impossibility",
"global",
"convergence",
"optimization",
"multi-loss",
"multi-player",
"multi-agent",
"gradient",
"descent"
] | null | 1,833 | 2005.12649 | title_snapshot |
OHgnfSrn2jv | Efficient Wasserstein Natural Gradients for Reinforcement Learning | https://openreview.net/forum?id=OHgnfSrn2jv | [
"Ted Moskovitz",
"Michael Arbel",
"Ferenc Huszar",
"Arthur Gretton"
] | Poster | null | A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally efficient \emph{Wasserstein natural gradient} (WNG) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed o... | [
"reinforcement learning",
"optimization"
] | null | 1,830 | 2010.05380 | title_snapshot |
PULSD5qI2N1 | Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | https://openreview.net/forum?id=PULSD5qI2N1 | [
"Atsushi Nitanda",
"Taiji Suzuki"
] | Oral | null | We analyze the convergence of the averaged stochastic gradient descent for overparameterized two-layer neural networks for regression problems. It was recently found that a neural tangent kernel (NTK) plays an important role in showing the global convergence of gradient-based methods under the NTK regime, where the lea... | [
"stochastic gradient descent",
"two-layer neural network",
"over-parameterization",
"neural tangent kernel"
] | null | 1,820 | 2006.12297 | title_snapshot |
3Aoft6NWFej | PMI-Masking: Principled masking of correlated spans | https://openreview.net/forum?id=3Aoft6NWFej | [
"Yoav Levine",
"Barak Lenz",
"Opher Lieber",
"Omri Abend",
"Kevin Leyton-Brown",
"Moshe Tennenholtz",
"Yoav Shoham"
] | Spotlight | null | Masking tokens uniformly at random constitutes a common flaw in the pretraining of Masked Language Models (MLMs) such as BERT. We show that such uniform masking allows an MLM to minimize its training objective by latching onto shallow local signals, leading to pretraining inefficiency and suboptimal downstream performa... | [
"Language modeling",
"BERT",
"pointwise mutual information"
] | null | 1,810 | 2010.01825 | title_snapshot |
C70cp4Cn32 | Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks | https://openreview.net/forum?id=C70cp4Cn32 | [
"Timothy Castiglia",
"Anirban Das",
"Stacy Patterson"
] | Poster | null | We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint sub-networks, with a single hub and multiple workers; further, workers may have differ... | [
"Machine Learning",
"Stochastic Gradient Descent",
"Federated Learning",
"Hierarchical Networks",
"Distributed",
"Heterogeneous",
"Convergence Analysis"
] | null | 1,804 | 2007.13819 | title_judge |
QoWatN-b8T | Kanerva++: Extending the Kanerva Machine With Differentiable, Locally Block Allocated Latent Memory | https://openreview.net/forum?id=QoWatN-b8T | [
"Jason Ramapuram",
"Yan Wu",
"Alexandros Kalousis"
] | Poster | null | Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (se... | [
"memory",
"generative model",
"latent variable",
"heap allocation"
] | null | 1,802 | 2103.03905 | title_snapshot |
hsFN92eQEla | EVALUATION OF NEURAL ARCHITECTURES TRAINED WITH SQUARE LOSS VS CROSS-ENTROPY IN CLASSIFICATION TASKS | https://openreview.net/forum?id=hsFN92eQEla | [
"Like Hui",
"Mikhail Belkin"
] | Poster | null | Modern neural architectures for classification tasks are trained using the cross-entropy loss, which is widely believed to be empirically superior to the square loss. In this work we provide evidence indicating that this belief may not be well-founded.
We explore several major neural architectures and a range of stand... | [
"large scale learning",
"square loss vs cross-entropy",
"classification",
"experimental evaluation"
] | null | 1,801 | 2006.07322 | title_snapshot |
-bdp_8Itjwp | Self-supervised Learning from a Multi-view Perspective | https://openreview.net/forum?id=-bdp_8Itjwp | [
"Yao-Hung Hubert Tsai",
"Yue Wu",
"Ruslan Salakhutdinov",
"Louis-Philippe Morency"
] | Poster | null | As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as object detection and image captioning. Many proposed approaches for self-supervised learning follow naturally a multi-... | [
"Self-supervised Learning",
"Unsupervised Learning",
"Multi-view Representation Learning"
] | null | 1,776 | 2006.05576 | title_snapshot |
j9Rv7qdXjd | Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels | https://openreview.net/forum?id=j9Rv7qdXjd | [
"Binxin Ru",
"Xingchen Wan",
"Xiaowen Dong",
"Michael Osborne"
] | Poster | null | Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or how we should modify the architecture if we want further improvements. We propose a Bayesian optimisation (BO) approach for NAS that combin... | [] | null | 1,775 | 2006.07556 | title_snapshot |
UoaQUQREMOs | CT-Net: Channel Tensorization Network for Video Classification | https://openreview.net/forum?id=UoaQUQREMOs | [
"Kunchang Li",
"Xianhang Li",
"Yali Wang",
"Jun Wang",
"Yu Qiao"
] | Poster | null | 3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions. Unfortunately, most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficiency... | [
"Video Classification",
"3D Convolution",
"Channel Tensorization"
] | null | 1,771 | 2106.01603 | title_snapshot |
-2FCwDKRREu | Learning Invariant Representations for Reinforcement Learning without Reconstruction | https://openreview.net/forum?id=-2FCwDKRREu | [
"Amy Zhang",
"Rowan Thomas McAllister",
"Roberto Calandra",
"Yarin Gal",
"Sergey Levine"
] | Oral | null | We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details. Bisimulatio... | [
"rich observations",
"bisimulation metrics",
"representation learning",
"state abstractions"
] | null | 1,770 | 2006.10742 | title_snapshot |
tqOvYpjPax2 | Intraclass clustering: an implicit learning ability that regularizes DNNs | https://openreview.net/forum?id=tqOvYpjPax2 | [
"Simon Carbonnelle",
"Christophe De Vleeschouwer"
] | Poster | null | Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood. In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitute... | [
"deep learning",
"generalization",
"implicit regularization"
] | null | 1,769 | 2103.06733 | title_snapshot |
fmOOI2a3tQP | Learning Robust State Abstractions for Hidden-Parameter Block MDPs | https://openreview.net/forum?id=fmOOI2a3tQP | [
"Amy Zhang",
"Shagun Sodhani",
"Khimya Khetarpal",
"Joelle Pineau"
] | Poster | null | Many control tasks exhibit similar dynamics that can be modeled as having common latent structure. Hidden-Parameter Markov Decision Processes (HiP-MDPs) explicitly model this structure to improve sample efficiency in multi-task settings.
However, this setting makes strong assumptions on the observability of the state t... | [
"multi-task reinforcement learning",
"bisimulation",
"hidden-parameter mdp",
"block mdp"
] | null | 1,767 | 2007.07206 | title_snapshot |
FX0vR39SJ5q | Isometric Transformation Invariant and Equivariant Graph Convolutional Networks | https://openreview.net/forum?id=FX0vR39SJ5q | [
"Masanobu Horie",
"Naoki Morita",
"Toshiaki Hishinuma",
"Yu Ihara",
"Naoto Mitsume"
] | Poster | null | Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as physical simulations. A crucial requirement for applying graphs in Euclidean spaces to physical simulations is lear... | [
"Machine Learning",
"Graph Neural Network",
"Invariance",
"Equivariance",
"Simulation",
"Mesh"
] | null | 1,757 | 2005.06316 | title_snapshot |
P6_q1BRxY8Q | Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates | https://openreview.net/forum?id=P6_q1BRxY8Q | [
"Zengyi Qin",
"Kaiqing Zhang",
"Yuxiao Chen",
"Jingkai Chen",
"Chuchu Fan"
] | Poster | null | We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals. Our core idea is to learn the multi-agent control policy jointly with learning the control barrier functions as safety certificates. We propose a new joint-l... | [
"Multi-agent",
"safe",
"control barrier function",
"reinforcement learning"
] | null | 1,756 | 2101.05436 | title_snapshot |
KUDUoRsEphu | Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize | https://openreview.net/forum?id=KUDUoRsEphu | [
"Nils Wandel",
"Michael Weinmann",
"Reinhard Klein"
] | Spotlight | null | Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer-generated imagery to computer-aided design in research and development. However, solving the partial differential equations of incompressible fluids is a challenging task and traditional numerical approximation scheme... | [
"Unsupervised Learning",
"Fluid Dynamics",
"U-Net"
] | null | 1,754 | 2006.08762 | title_snapshot |
xCxXwTzx4L1 | ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations | https://openreview.net/forum?id=xCxXwTzx4L1 | [
"Rishabh Tiwari",
"Udbhav Bamba",
"Arnav Chavan",
"Deepak Gupta"
] | Poster | null | Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy. However, most existing methods still suffer from one or more limitations, that include 1) the need for training the dense model... | [
"Structured Pruning",
"Budget-Aware Pruning",
"Budget constraints",
"Sparsity Learning"
] | null | 1,752 | 2102.07156 | title_snapshot |
Drynvt7gg4L | AdaSpeech: Adaptive Text to Speech for Custom Voice | https://openreview.net/forum?id=Drynvt7gg4L | [
"Mingjian Chen",
"Xu Tan",
"Bohan Li",
"Yanqing Liu",
"Tao Qin",
"sheng zhao",
"Tie-Yan Liu"
] | Poster | null | Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech from her/him. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model ne... | [
"Text to speech",
"adaptation",
"fine-tuning",
"custom voice",
"acoustic condition modeling",
"conditional layer normalization"
] | null | 1,751 | 2103.00993 | title_snapshot |
YLewtnvKgR7 | Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors | https://openreview.net/forum?id=YLewtnvKgR7 | [
"Ali Harakeh",
"Steven L. Waslander"
] | Poster | null | Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training va... | [
"Object Detection",
"Predictive Uncertainty Estimation",
"Proper Scoring Rules",
"Variance Networks",
"Energy Score",
"Computer Vision"
] | null | 1,741 | 2101.05036 | title_snapshot |
QubpWYfdNry | Domain-Robust Visual Imitation Learning with Mutual Information Constraints | https://openreview.net/forum?id=QubpWYfdNry | [
"Edoardo Cetin",
"Oya Celiktutan"
] | Poster | null | Human beings are able to understand objectives and learn by simply observing others perform a task. Imitation learning methods aim to replicate such capabilities, however, they generally depend on access to a full set of optimal states and actions taken with the agent's actuators and from the agent's point of view. In ... | [
"Imitation Learning",
"Reinforcement Learning",
"Observational Imitation",
"Third-Person Imitation",
"Mutual Information",
"Domain Adaption",
"Machine Learning"
] | null | 1,735 | 2103.05079 | title_snapshot |
e12NDM7wkEY | Clustering-friendly Representation Learning via Instance Discrimination and Feature Decorrelation | https://openreview.net/forum?id=e12NDM7wkEY | [
"Yaling Tao",
"Kentaro Takagi",
"Kouta Nakata"
] | Poster | null | Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we... | [
"clustering",
"representation learning",
"deep embedding"
] | null | 1,734 | 2106.00131 | title_snapshot |
rumv7QmLUue | A Gradient Flow Framework For Analyzing Network Pruning | https://openreview.net/forum?id=rumv7QmLUue | [
"Ekdeep Singh Lubana",
"Robert P. Dick"
] | Spotlight | null | Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed to prune trained models. Despite lacking justification for their use early-on in training, such measures result in surprisingly... | [
"Network pruning",
"Gradient flow",
"Early pruning"
] | null | 1,732 | 2009.11839 | title_snapshot |
9GsFOUyUPi | Progressive Skeletonization: Trimming more fat from a network at initialization | https://openreview.net/forum?id=9GsFOUyUPi | [
"Pau de Jorge",
"Amartya Sanyal",
"Harkirat Behl",
"Philip Torr",
"Grégory Rogez",
"Puneet K. Dokania"
] | Poster | null | Recent studies have shown that skeletonization (pruning parameters) of networks at initialization provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance. However, we observe that beyond a certain level of sparsity (approx 95%), these approa... | [
"Pruning",
"Pruning at initialization",
"Sparsity"
] | null | 1,729 | 2006.09081 | title_snapshot |
8Sqhl-nF50 | On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis | https://openreview.net/forum?id=8Sqhl-nF50 | [
"Zhong Li",
"Jiequn Han",
"Weinan E",
"Qianxiao Li"
] | Poster | null | We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data. We consider the simple but representative setting of using continuous-time linear RNNs to learn from data generated by linear relationships. Mathematical... | [
"recurrent neural network",
"dynamical system",
"universal approximation",
"optimization",
"curse of memory"
] | null | 1,725 | 2009.07799 | title_snapshot |
7aogOj_VYO0 | Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning | https://openreview.net/forum?id=7aogOj_VYO0 | [
"Da Yu",
"Huishuai Zhang",
"Wei Chen",
"Tie-Yan Liu"
] | Poster | null | The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private model degrades the utility drastically when the model comprises a large number of trainable parameters. In this paper, we propose an algori... | [
"privacy preserving machine learning",
"differentially private deep learning",
"gradient redundancy"
] | null | 1,722 | 2102.12677 | title_snapshot |
z5Z023VBmDZ | More or Less: When and How to Build Convolutional Neural Network Ensembles | https://openreview.net/forum?id=z5Z023VBmDZ | [
"Abdul Wasay",
"Stratos Idreos"
] | Poster | null | Convolutional neural networks are utilized to solve increasingly more complex problems and with more data. As a result, researchers and practitioners seek to scale the representational power of such models by adding more parameters. However, increasing parameters requires additional critical resources in terms of memo... | [
"ensemble learning",
"empirical study",
"machine learning systems",
"computer vision"
] | null | 1,719 | null | null |
ks5nebunVn_ | Towards Robustness Against Natural Language Word Substitutions | https://openreview.net/forum?id=ks5nebunVn_ | [
"Xinshuai Dong",
"Anh Tuan Luu",
"Rongrong Ji",
"Hong Liu"
] | Spotlight | null | Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing. Previous defense methods capture word substitutions in vector... | [
"Natural Language Processing",
"Adversarial Defense"
] | null | 1,717 | 2107.13541 | title_snapshot |
3RLN4EPMdYd | Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction | https://openreview.net/forum?id=3RLN4EPMdYd | [
"Wonkwang Lee",
"Whie Jung",
"Han Zhang",
"Ting Chen",
"Jing Yu Koh",
"Thomas Huang",
"Hyungsuk Yoon",
"Honglak Lee",
"Seunghoon Hong"
] | Poster | null | Learning to predict the long-term future of video frames is notoriously challenging due to the inherent ambiguities in a distant future and dramatic amplification of prediction error over time. Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less than ... | [
"Video prediction",
"generative model",
"long-term prediction"
] | null | 1,712 | 2104.06697 | title_snapshot |
jEYKjPE1xYN | Symmetry-Aware Actor-Critic for 3D Molecular Design | https://openreview.net/forum?id=jEYKjPE1xYN | [
"Gregor N. C. Simm",
"Robert Pinsler",
"Gábor Csányi",
"José Miguel Hernández-Lobato"
] | Poster | null | Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light o... | [
"deep reinforcement learning",
"molecular design",
"covariant neural networks"
] | null | 1,710 | 2011.12747 | title_snapshot |
vQzcqQWIS0q | Learnable Embedding sizes for Recommender Systems | https://openreview.net/forum?id=vQzcqQWIS0q | [
"Siyi Liu",
"Chen Gao",
"Yihong Chen",
"Depeng Jin",
"Yong Li"
] | Poster | null | The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table tha... | [
"Recommender Systems",
"Deep Learning",
"Embedding Size"
] | null | 1,708 | 2101.07577 | title_snapshot |
R4aWTjmrEKM | Iterative Empirical Game Solving via Single Policy Best Response | https://openreview.net/forum?id=R4aWTjmrEKM | [
"Max Smith",
"Thomas Anthony",
"Michael Wellman"
] | Spotlight | null | Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (DRL).
At each iteration, DRL is invoked to train a best response to a mixture of opponent policies.
The repeated application of DR... | [
"Empirical Game Theory",
"Reinforcement Learning",
"Multiagent Learning"
] | null | 1,701 | 2106.01901 | title_snapshot |
pAbm1qfheGk | Learning Neural Generative Dynamics for Molecular Conformation Generation | https://openreview.net/forum?id=pAbm1qfheGk | [
"Minkai Xu",
"Shitong Luo",
"Yoshua Bengio",
"Jian Peng",
"Jian Tang"
] | Poster | null | We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation d... | [
"Molecular conformation generation",
"deep generative models",
"continuous normalizing flow",
"energy-based models"
] | null | 1,699 | 2102.10240 | title_snapshot |
NjF772F4ZZR | Learning the Pareto Front with Hypernetworks | https://openreview.net/forum?id=NjF772F4ZZR | [
"Aviv Navon",
"Aviv Shamsian",
"Ethan Fetaya",
"Gal Chechik"
] | Poster | null | Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off between possibly conflicting objectives. Recent MOO methods can target a specific desired ray in loss spa... | [
"Multi-objective optimization",
"multi-task learning"
] | null | 1,698 | 2010.04104 | title_snapshot |
Y87Ri-GNHYu | Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning | https://openreview.net/forum?id=Y87Ri-GNHYu | [
"Valerie Chen",
"Abhinav Gupta",
"Kenneth Marino"
] | Poster | null | Complex, multi-task problems have proven to be difficult to solve efficiently in a sparse-reward reinforcement learning setting. In order to be sample efficient, multi-task learning requires reuse and sharing of low-level policies. To facilitate the automatic decomposition of hierarchical tasks, we propose the use of s... | [] | null | 1,692 | 2011.00517 | title_snapshot |
ZW0yXJyNmoG | Taming GANs with Lookahead-Minmax | https://openreview.net/forum?id=ZW0yXJyNmoG | [
"Tatjana Chavdarova",
"Matteo Pagliardini",
"Sebastian U Stich",
"François Fleuret",
"Martin Jaggi"
] | Poster | null | Generative Adversarial Networks are notoriously challenging to train. The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. To tackle these challenges, we propose the Lookahead algorithm for minmax optimizati... | [
"Minmax",
"Generative Adversarial Networks"
] | null | 1,684 | 2006.14567 | title_snapshot |
1Jv6b0Zq3qi | Uncertainty in Gradient Boosting via Ensembles | https://openreview.net/forum?id=1Jv6b0Zq3qi | [
"Andrey Malinin",
"Liudmila Prokhorenkova",
"Aleksei Ustimenko"
] | Poster | null | For many practical, high-risk applications, it is essential to quantify uncertainty in a model's predictions to avoid costly mistakes. While predictive uncertainty is widely studied for neural networks, the topic seems to be under-explored for models based on gradient boosting. However, gradient boosting often achieves... | [
"uncertainty",
"ensembles",
"gradient boosting",
"decision trees",
"knowledge uncertainty"
] | null | 1,680 | 2006.10562 | title_snapshot |
eLfqMl3z3lq | Adversarial score matching and improved sampling for image generation | https://openreview.net/forum?id=eLfqMl3z3lq | [
"Alexia Jolicoeur-Martineau",
"Rémi Piché-Taillefer",
"Ioannis Mitliagkas",
"Remi Tachet des Combes"
] | Poster | null | Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by first training a neural network to estimate the score of a distribution, and then using Langevin dynamics to sample from the data distribution assumed by the score network. Despite... | [
"adversarial",
"score matching",
"Langevin dynamics",
"GAN",
"generative model"
] | null | 1,676 | 2009.05475 | title_snapshot |
piLPYqxtWuA | FastSpeech 2: Fast and High-Quality End-to-End Text to Speech | https://openreview.net/forum?id=piLPYqxtWuA | [
"Yi Ren",
"Chenxu Hu",
"Xu Tan",
"Tao Qin",
"Sheng Zhao",
"Zhou Zhao",
"Tie-Yan Liu"
] | Poster | null | Non-autoregressive text to speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledg... | [
"text to speech",
"speech synthesis",
"non-autoregressive generation",
"one-to-many mapping",
"end-to-end"
] | null | 1,673 | 2006.04558 | title_snapshot |
DEa4JdMWRHp | Interpretable Models for Granger Causality Using Self-explaining Neural Networks | https://openreview.net/forum?id=DEa4JdMWRHp | [
"Ričards Marcinkevičs",
"Julia E Vogt"
] | Poster | null | Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality u... | [
"time series",
"Granger causality",
"interpretability",
"inference",
"neural networks"
] | null | 1,672 | 2101.07600 | title_snapshot |
sy4Kg_ZQmS7 | Learning Deep Features in Instrumental Variable Regression | https://openreview.net/forum?id=sy4Kg_ZQmS7 | [
"Liyuan Xu",
"Yutian Chen",
"Siddarth Srinivasan",
"Nando de Freitas",
"Arnaud Doucet",
"Arthur Gretton"
] | Poster | null | Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: ... | [
"Causal Inference",
"Instrumental Variable Regression",
"Deep Learning",
"Reinforcement Learning"
] | null | 1,670 | 2010.07154 | title_snapshot |
z9k8BWL-_2u | Statistical inference for individual fairness | https://openreview.net/forum?id=z9k8BWL-_2u | [
"Subha Maity",
"Songkai Xue",
"Mikhail Yurochkin",
"Yuekai Sun"
] | Poster | null | As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating unwanted social biases has come to the fore of the public's and the research community's attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We ... | [] | null | 1,665 | 2103.16714 | title_snapshot |
o_V-MjyyGV_ | Self-Supervised Policy Adaptation during Deployment | https://openreview.net/forum?id=o_V-MjyyGV_ | [
"Nicklas Hansen",
"Rishabh Jangir",
"Yu Sun",
"Guillem Alenyà",
"Pieter Abbeel",
"Alexei A Efros",
"Lerrel Pinto",
"Xiaolong Wang"
] | Spotlight | null | In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new enviro... | [
"reinforcement learning",
"robotics",
"self-supervised learning",
"generalization",
"sim2real"
] | null | 1,664 | 2007.04309 | title_snapshot |
AY8zfZm0tDd | Randomized Ensembled Double Q-Learning: Learning Fast Without a Model | https://openreview.net/forum?id=AY8zfZm0tDd | [
"Xinyue Chen",
"Che Wang",
"Zijian Zhou",
"Keith W. Ross"
] | Poster | null | Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple model-free algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance... | [
"Artificial Integlligence",
"Machine Learning",
"Deep Reinforcement Learning"
] | null | 1,662 | 2101.05982 | title_snapshot |
ADWd4TJO13G | Lifelong Learning of Compositional Structures | https://openreview.net/forum?id=ADWd4TJO13G | [
"Jorge A Mendez",
"ERIC EATON"
] | Poster | null | A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and adequately reuse them in novel combinations for solving different yet structurally related problems. Learning such compositional structures has been a significant challenge for artificial systems, due to the combinatoria... | [
"lifelong learning",
"continual learning",
"compositional learning",
"modular networks"
] | null | 1,652 | 2007.07732 | title_snapshot |
Rcmk0xxIQV | QPLEX: Duplex Dueling Multi-Agent Q-Learning | https://openreview.net/forum?id=Rcmk0xxIQV | [
"Jianhao Wang",
"Zhizhou Ren",
"Terry Liu",
"Yang Yu",
"Chongjie Zhang"
] | Poster | null | We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local ... | [
"Multi-agent reinforcement learning",
"Value factorization",
"Dueling structure"
] | null | 1,649 | 2008.01062 | title_snapshot |
--gvHfE3Xf5 | Meta-Learning of Structured Task Distributions in Humans and Machines | https://openreview.net/forum?id=--gvHfE3Xf5 | [
"Sreejan Kumar",
"Ishita Dasgupta",
"Jonathan Cohen",
"Nathaniel Daw",
"Thomas Griffiths"
] | Poster | null | In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks that were previously assumed to require structured representations, making strides toward closing the gap between humans and machines. Howe... | [
"meta-learning",
"human cognition",
"reinforcement learning",
"compositionality"
] | null | 1,648 | 2010.02317 | title_snapshot |
YTWGvpFOQD- | Differentially Private Learning Needs Better Features (or Much More Data) | https://openreview.net/forum?id=YTWGvpFOQD- | [
"Florian Tramer",
"Dan Boneh"
] | Spotlight | null | We demonstrate that differentially private machine learning has not yet reached its ''AlexNet moment'' on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets.
To exceed the performance of handcrafted features, w... | [
"Differential Privacy",
"Privacy",
"Deep Learning"
] | null | 1,641 | 2011.11660 | title_snapshot |
8X2eaSZxTP | PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds | https://openreview.net/forum?id=8X2eaSZxTP | [
"Yujia Liu",
"Stefano D'Aronco",
"Konrad Schindler",
"Jan Dirk Wegner"
] | Poster | null | We introduce PC2WF, the first end-to-end trainable deep network architecture to convert a 3D point cloud into a wireframe model. The network takes as input an unordered set of 3D points sampled from the surface of some object, and outputs a wireframe of that object, i.e., a sparse set of corner points linked by line se... | [
"deep neural network",
"3d point cloud",
"wireframe model"
] | null | 1,636 | 2103.02766 | title_snapshot |
dYeAHXnpWJ4 | Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability | https://openreview.net/forum?id=dYeAHXnpWJ4 | [
"Suraj Srinivas",
"Francois Fleuret"
] | Oral | null | Current methods for the interpretability of discriminative deep neural networks commonly rely on the model's input-gradients, i.e., the gradients of the output logits w.r.t. the inputs. The common assumption is that these input-gradients contain information regarding $p_{\theta} ( y\mid \mathbf{x} )$, the model's discr... | [
"Interpretability",
"saliency maps",
"score-matching"
] | null | 1,633 | 2006.09128 | title_snapshot |
9l0K4OM-oXE | Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks | https://openreview.net/forum?id=9l0K4OM-oXE | [
"Yige Li",
"Xixiang Lyu",
"Nodens Koren",
"Lingjuan Lyu",
"Bo Li",
"Xingjun Ma"
] | Poster | null | Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean ... | [
"Backdoor Defense",
"Deep Neural Networks",
"Neural Attention Distillation"
] | null | 1,631 | 2101.05930 | title_snapshot |
uCQfPZwRaUu | Data-Efficient Reinforcement Learning with Self-Predictive Representations | https://openreview.net/forum?id=uCQfPZwRaUu | [
"Max Schwarzer",
"Ankesh Anand",
"Rishab Goel",
"R Devon Hjelm",
"Aaron Courville",
"Philip Bachman"
] | Spotlight | null | While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-su... | [
"Reinforcement Learning",
"Self-Supervised Learning",
"Representation Learning",
"Sample Efficiency"
] | null | 1,629 | 2007.05929 | title_snapshot |
hiq1rHO8pNT | HyperGrid Transformers: Towards A Single Model for Multiple Tasks | https://openreview.net/forum?id=hiq1rHO8pNT | [
"Yi Tay",
"Zhe Zhao",
"Dara Bahri",
"Donald Metzler",
"Da-Cheng Juan"
] | Poster | null | Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a higher overall parameter cost, along with higher technical maintenance for serving multiple models. Learning a single multi-task model that ... | [
"Transformers",
"Multi-Task Learning"
] | null | 1,622 | null | null |
qVyeW-grC2k | Long Range Arena : A Benchmark for Efficient Transformers | https://openreview.net/forum?id=qVyeW-grC2k | [
"Yi Tay",
"Mostafa Dehghani",
"Samira Abnar",
"Yikang Shen",
"Dara Bahri",
"Philip Pham",
"Jinfeng Rao",
"Liu Yang",
"Sebastian Ruder",
"Donald Metzler"
] | Poster | null | Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer m... | [
"Transformers",
"Attention",
"Deep Learning"
] | null | 1,616 | 2011.04006 | title_snapshot |
j1RMMKeP2gR | Acting in Delayed Environments with Non-Stationary Markov Policies | https://openreview.net/forum?id=j1RMMKeP2gR | [
"Esther Derman",
"Gal Dalal",
"Shie Mannor"
] | Poster | null | The standard Markov Decision Process (MDP) formulation hinges on the assumption that an action is executed immediately after it was chosen. However, assuming it is often unrealistic and can lead to catastrophic failures in applications such as robotic manipulation, cloud computing, and finance. We introduce a framework... | [
"reinforcement learning",
"delay"
] | null | 1,615 | 2101.11992 | title_snapshot |
q_S44KLQ_Aa | Neurally Augmented ALISTA | https://openreview.net/forum?id=q_S44KLQ_Aa | [
"Freya Behrens",
"Jonathan Sauder",
"Peter Jung"
] | Poster | null | It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been... | [
"compressed sensing",
"sparse reconstruction",
"unrolled algorithms",
"learned ISTA"
] | null | 1,614 | 2010.01930 | title_snapshot |
vhKe9UFbrJo | Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models | https://openreview.net/forum?id=vhKe9UFbrJo | [
"Yuge Shi",
"Brooks Paige",
"Philip Torr",
"Siddharth N"
] | Poster | null | Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language. While it has proven effective for learning generalisable representations, the training of such models often requires a large amount of rela... | [
"Deep generative model",
"multi-modal learning",
"representation learning"
] | null | 1,601 | 2007.01179 | title_snapshot |
-GLNZeVDuik | Categorical Normalizing Flows via Continuous Transformations | https://openreview.net/forum?id=-GLNZeVDuik | [
"Phillip Lippe",
"Efstratios Gavves"
] | Poster | null | Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a continuous space is inapplicable as categorical data has no intrinsic order. Instead, categorical data have complex and latent relations that ... | [
"Normalizing Flows",
"Density Estimation",
"Graph Generation"
] | null | 1,592 | 2006.09790 | title_snapshot |
aCgLmfhIy_f | Prototypical Representation Learning for Relation Extraction | https://openreview.net/forum?id=aCgLmfhIy_f | [
"Ning Ding",
"Xiaobin Wang",
"Yao Fu",
"Guangwei Xu",
"Rui Wang",
"Pengjun Xie",
"Ying Shen",
"Fei Huang",
"Hai-Tao Zheng",
"Rui Zhang"
] | Poster | null | Recognizing relations between entities is a pivotal task of relational learning.
Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language.
This paper aims to learn predictive, interpretable, and robust relation r... | [
"NLP",
"Relation Extraction",
"Representation Learning"
] | null | 1,588 | 2103.11647 | title_snapshot |
0PtUPB9z6qK | Generalized Energy Based Models | https://openreview.net/forum?id=0PtUPB9z6qK | [
"Michael Arbel",
"Liang Zhou",
"Arthur Gretton"
] | Poster | null | We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probabilit... | [
"Sampling",
"MCMC",
"Generative Models",
"Adversarial training",
"Optimization",
"Density estimation"
] | null | 1,585 | 2003.05033 | title_snapshot |
Y9McSeEaqUh | Predicting Classification Accuracy When Adding New Unobserved Classes | https://openreview.net/forum?id=Y9McSeEaqUh | [
"Yuli Slavutsky",
"Yuval Benjamini"
] | Poster | null | Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier’s performance over the initial class sample can be used to extrapolate its expected accuracy on a larger... | [
"multiclass classification",
"classification",
"extrapolation",
"accuracy",
"ROC"
] | null | 1,583 | 2010.15011 | title_snapshot |
lQdXeXDoWtI | In Search of Lost Domain Generalization | https://openreview.net/forum?id=lQdXeXDoWtI | [
"Ishaan Gulrajani",
"David Lopez-Paz"
] | Poster | null | The goal of domain generalization algorithms is to predict well on distributions different from those seen during training.
While a myriad of domain generalization algorithms exist, inconsistencies in experimental conditions---datasets, network architectures, and model selection criteria---render fair comparisons diffi... | [
"domain generalization",
"reproducible research"
] | null | 1,580 | 2007.01434 | title_snapshot |
kEnBH98BGs5 | Estimating informativeness of samples with Smooth Unique Information | https://openreview.net/forum?id=kEnBH98BGs5 | [
"Hrayr Harutyunyan",
"Alessandro Achille",
"Giovanni Paolini",
"Orchid Majumder",
"Avinash Ravichandran",
"Rahul Bhotika",
"Stefano Soatto"
] | Poster | null | We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights. Though related, we show that these quantities have a qualitatively dif... | [
"sample information",
"information theory",
"stability theory",
"ntk",
"dataset summarization"
] | null | 1,579 | 2101.06640 | title_snapshot |
45NZvF1UHam | Identifying Physical Law of Hamiltonian Systems via Meta-Learning | https://openreview.net/forum?id=45NZvF1UHam | [
"Seungjun Lee",
"Haesang Yang",
"Woojae Seong"
] | Poster | null | Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying... | [
"Learning physical laws",
"meta-learning",
"Hamiltonian systems"
] | null | 1,571 | 2102.11544 | title_snapshot |
dyjPVUc2KB | Adapting to Reward Progressivity via Spectral Reinforcement Learning | https://openreview.net/forum?id=dyjPVUc2KB | [
"Michael Dann",
"John Thangarajah"
] | Poster | null | In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively u... | [
"Reinforcement Learning",
"Deep Reinforcement Learning"
] | null | 1,570 | 2104.14138 | title_snapshot |
rsogjAnYs4z | Understanding the effects of data parallelism and sparsity on neural network training | https://openreview.net/forum?id=rsogjAnYs4z | [
"Namhoon Lee",
"Thalaiyasingam Ajanthan",
"Philip Torr",
"Martin Jaggi"
] | Poster | null | We study two factors in neural network training: data parallelism and sparsity; here, data parallelism means processing training data in parallel using distributed systems (or equivalently increasing batch size), so that training can be accelerated; for sparsity, we refer to pruning parameters in a neural network model... | [
"data parallelism",
"sparsity",
"neural network training"
] | null | 1,569 | 2003.11316 | title_snapshot |
TtYSU29zgR | Primal Wasserstein Imitation Learning | https://openreview.net/forum?id=TtYSU29zgR | [
"Robert Dadashi",
"Leonard Hussenot",
"Matthieu Geist",
"Olivier Pietquin"
] | Poster | null | Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agen... | [
"Reinforcement Learning",
"Inverse Reinforcement Learning",
"Imitation Learning",
"Optimal Transport",
"Wasserstein distance"
] | null | 1,564 | 2006.04678 | title_snapshot |
Ptaz_zIFbX | Prediction and generalisation over directed actions by grid cells | https://openreview.net/forum?id=Ptaz_zIFbX | [
"Changmin Yu",
"Timothy Behrens",
"Neil Burgess"
] | Poster | null | Knowing how the effects of directed actions generalise to new situations (e.g. moving North, South, East and West, or turning left, right, etc.) is key to rapid generalisation across new situations. Markovian tasks can be characterised by a state space and a transition matrix and recent work has proposed that neural gr... | [
"Computational neuroscience",
"grid cells",
"normative models"
] | null | 1,555 | 2006.03355 | title_snapshot |
1rxHOBjeDUW | Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration | https://openreview.net/forum?id=1rxHOBjeDUW | [
"Jaekyeom Kim",
"Minjung Kim",
"Dongyeon Woo",
"Gunhee Kim"
] | Poster | null | We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable. Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input vari... | [
"Reinforcement learning",
"Information bottleneck"
] | null | 1,552 | 2103.12300 | title_snapshot |
umIdUL8rMH | BOIL: Towards Representation Change for Few-shot Learning | https://openreview.net/forum?id=umIdUL8rMH | [
"Jaehoon Oh",
"Hyungjun Yoo",
"ChangHwan Kim",
"Se-Young Yun"
] | Poster | null | Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates from a meta-initialization point and learns the meta-initialization parameters with outer updates. It has recently been hypothesized that re... | [] | null | 1,549 | 2008.08882 | title_snapshot |
ee6W5UgQLa | MultiModalQA: complex question answering over text, tables and images | https://openreview.net/forum?id=ee6W5UgQLa | [
"Alon Talmor",
"Ori Yoran",
"Amnon Catav",
"Dan Lahav",
"Yizhong Wang",
"Akari Asai",
"Gabriel Ilharco",
"Hannaneh Hajishirzi",
"Jonathan Berant"
] | Poster | null | When answering complex questions, people can seamlessly combine information from visual, textual and tabular sources.
While interest in models that reason over multiple pieces of evidence has surged in recent years, there has been relatively little work on question answering models that reason across multiple modaliti... | [
"NLP",
"Question Answering",
"Dataset",
"Multi-Modal",
"Multi-Hop"
] | null | 1,545 | 2104.06039 | title_snapshot |
EoFNy62JGd | Neural gradients are near-lognormal: improved quantized and sparse training | https://openreview.net/forum?id=EoFNy62JGd | [
"Brian Chmiel",
"Liad Ben-Uri",
"Moran Shkolnik",
"Elad Hoffer",
"Ron Banner",
"Daniel Soudry"
] | Poster | null | While training can mostly be accelerated by reducing the time needed to propagate neural gradients (loss gradients with respect to the intermediate neural layer outputs) back throughout the model, most previous works focus on the quantization/pruning of weights and activations. These methods are often not applicable to... | [] | null | 1,539 | 2006.08173 | title_snapshot |
e8W-hsu_q5 | Group Equivariant Conditional Neural Processes | https://openreview.net/forum?id=e8W-hsu_q5 | [
"Makoto Kawano",
"Wataru Kumagai",
"Akiyoshi Sannai",
"Yusuke Iwasawa",
"Yutaka Matsuo"
] | Poster | null | We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equiv... | [
"Neural Processes",
"Conditional Neural Processes",
"Stochastic Processes",
"Regression",
"Group Equivariance",
"Symmetry"
] | null | 1,535 | 2102.08759 | title_snapshot |
3hGNqpI4WS | Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization | https://openreview.net/forum?id=3hGNqpI4WS | [
"Tatsuya Matsushima",
"Hiroki Furuta",
"Yutaka Matsuo",
"Ofir Nachum",
"Shixiang Gu"
] | Poster | null | Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of de... | [
"Reinforcement Learning",
"deployment-efficiency",
"offline RL",
"Model-based RL"
] | null | 1,529 | 2006.03647 | title_snapshot |
tnSo6VRLmT | Efficient Conformal Prediction via Cascaded Inference with Expanded Admission | https://openreview.net/forum?id=tnSo6VRLmT | [
"Adam Fisch",
"Tal Schuster",
"Tommi S. Jaakkola",
"Regina Barzilay"
] | Poster | null | In this paper, we present a novel approach for conformal prediction (CP), in which we aim to identify a set of promising prediction candidates---in place of a single prediction. This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks. In the ... | [
"conformal prediction",
"uncertainty estimation",
"efficient inference methods",
"natural language processing",
"chemistry"
] | null | 1,527 | 2007.03114 | title_snapshot |
sTeoJiB4uR | Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks | https://openreview.net/forum?id=sTeoJiB4uR | [
"Thomas Bird",
"Friso Kingma",
"David Barber"
] | Poster | null | Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost in memory and execution time grows. Using binary weights in neural networks is one method which has shown promise in reducing this cost. Ho... | [
"binary",
"generative",
"optimization",
"compression"
] | null | 1,523 | 2010.13476 | title_snapshot |
0aW6lYOYB7d | Large-width functional asymptotics for deep Gaussian neural networks | https://openreview.net/forum?id=0aW6lYOYB7d | [
"Daniele Bracale",
"Stefano Favaro",
"Sandra Fortini",
"Stefano Peluchetti"
] | Poster | null | In this paper, we consider fully connected feed-forward deep neural networks where weights and biases are independent and identically distributed according to Gaussian distributions. Extending previous results (Matthews et al., 2018a;b;Yang, 2019) we adopt a function-space perspective, i.e. we look at neural networks ... | [
"deep learning theory",
"infinitely wide neural network",
"Gaussian process",
"stochastic process"
] | null | 1,511 | 2102.10307 | title_snapshot |
9z_dNsC4B5t | MetaNorm: Learning to Normalize Few-Shot Batches Across Domains | https://openreview.net/forum?id=9z_dNsC4B5t | [
"Yingjun Du",
"Xiantong Zhen",
"Ling Shao",
"Cees G. M. Snoek"
] | Poster | null | Batch normalization plays a crucial role when training deep neural networks. However, batch statistics become unstable with small batch sizes and are unreliable in the presence of distribution shifts. We propose MetaNorm, a simple yet effective meta-learning normalization. It tackles the aforementioned issues in a unif... | [
"Meta-learning",
"batch normalization",
"few-shot domain generalization"
] | null | 1,508 | null | null |
QpNz8r_Ri2Y | Representation Balancing Offline Model-based Reinforcement Learning | https://openreview.net/forum?id=QpNz8r_Ri2Y | [
"Byung-Jun Lee",
"Jongmin Lee",
"Kee-Eung Kim"
] | Poster | null | One of the main challenges in offline and off-policy reinforcement learning is to cope with the distribution shift that arises from the mismatch between the target policy and the data collection policy. In this paper, we focus on a model-based approach, particularly on learning the representation for a robust model of ... | [
"Reinforcement Learning",
"Model-based Reinforcement Learning",
"Offline Reinforcement Learning",
"Batch Reinforcement Learning",
"Off-policy policy evaluation"
] | null | 1,498 | null | null |
AWOSz_mMAPx | Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation | https://openreview.net/forum?id=AWOSz_mMAPx | [
"Tanner Fiez",
"Lillian J Ratliff"
] | Poster | null | We study the role that a finite timescale separation parameter $\tau$ has on gradient descent-ascent in non-convex, non-concave zero-sum games where the learning rate of player 1 is denoted by $\gamma_1$ and the learning rate of player 2 is defined to be $\gamma_2=\tau\gamma_1$. We provide a non-asymptotic construction... | [
"game theory",
"continuous games",
"generative adversarial networks",
"theory",
"gradient descent-ascent",
"equilibrium",
"convergence"
] | null | 1,497 | 2009.14820 | title_judge |
wS0UFjsNYjn | Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning | https://openreview.net/forum?id=wS0UFjsNYjn | [
"Dong Bok Lee",
"Dongchan Min",
"Seanie Lee",
"Sung Ju Hwang"
] | Spotlight | null | Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to generalize across tasks such that the learned model can rapidly adapt to a novel task, shares the spir... | [
"Unsupervised Learning",
"Meta-Learning",
"Unsupervised Meta-learning",
"Variational Autoencoders"
] | null | 1,492 | null | null |
E3Ys6a1NTGT | The Importance of Pessimism in Fixed-Dataset Policy Optimization | https://openreview.net/forum?id=E3Ys6a1NTGT | [
"Jacob Buckman",
"Carles Gelada",
"Marc G Bellemare"
] | Poster | null | We study worst-case guarantees on the expected return of fixed-dataset policy optimization algorithms. Our core contribution is a unified conceptual and mathematical framework for the study of algorithms in this regime. This analysis reveals that for naive approaches, the possibility of erroneous value overestimation l... | [
"deep learning",
"reinforcement learning",
"offline reinforcement learning"
] | null | 1,484 | 2009.06799 | title_snapshot |
9EKHN1jOlA | Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs | https://openreview.net/forum?id=9EKHN1jOlA | [
"Cheng Wang",
"Carolin Lawrence",
"Mathias Niepert"
] | Poster | null | Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stochastic discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several ti... | [
"uncertainty estimation",
"calibration",
"RNN"
] | null | 1,483 | 2011.12010 | title_snapshot |
jrA5GAccy_ | Empirical or Invariant Risk Minimization? A Sample Complexity Perspective | https://openreview.net/forum?id=jrA5GAccy_ | [
"Kartik Ahuja",
"Jun Wang",
"Amit Dhurandhar",
"Karthikeyan Shanmugam",
"Kush R. Varshney"
] | Poster | null | Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization. However, it is unclear when IRM should be preferred over the widely-employed empirical risk minimization (ERM) framework. In this work, we analyze both these frameworks from the perspect... | [
"invariant risk minimization",
"IRM"
] | null | 1,481 | 2010.16412 | title_snapshot |
zeFrfgyZln | Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | https://openreview.net/forum?id=zeFrfgyZln | [
"Lee Xiong",
"Chenyan Xiong",
"Ye Li",
"Kwok-Fung Tang",
"Jialin Liu",
"Paul N. Bennett",
"Junaid Ahmed",
"Arnold Overwijk"
] | Poster | null | Conducting text retrieval in a learned dense representation space has many intriguing advantages. Yet dense retrieval (DR) often underperforms word-based sparse retrieval. In this paper, we first theoretically show the bottleneck of dense retrieval is the domination of uninformative negatives sampled in mini-batch trai... | [
"Dense Retrieval",
"Text Retrieval",
"Text Representation",
"Neural IR"
] | null | 1,477 | 2007.00808 | title_snapshot |
0N8jUH4JMv6 | Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time | https://openreview.net/forum?id=0N8jUH4JMv6 | [
"Tolga Ergen",
"Mert Pilanci"
] | Spotlight | null | We study training of Convolutional Neural Networks (CNNs) with ReLU activations and introduce exact convex optimization formulations with a polynomial complexity with respect to the number of data samples, the number of neurons, and data dimension. More specifically, we develop a convex analytic framework utilizing sem... | [
"Convex optimization",
"non-convex optimization",
"group sparsity",
"$\\ell_1$ norm",
"convex duality",
"polynomial time",
"deep learning"
] | null | 1,468 | 2006.14798 | title_snapshot |
YNnpaAKeCfx | FairBatch: Batch Selection for Model Fairness | https://openreview.net/forum?id=YNnpaAKeCfx | [
"Yuji Roh",
"Kangwook Lee",
"Steven Euijong Whang",
"Changho Suh"
] | Poster | null | Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preprocessing or model training, rendering themselves difficult-to-adopt for potentially already complex machine learning systems. We address this p... | [
"model fairness",
"bilevel optimization",
"batch selection"
] | null | 1,462 | 2012.01696 | title_snapshot |
tIjRAiFmU3y | An Unsupervised Deep Learning Approach for Real-World Image Denoising | https://openreview.net/forum?id=tIjRAiFmU3y | [
"Dihan Zheng",
"Sia Huat Tan",
"Xiaowen Zhang",
"Zuoqiang Shi",
"Kaisheng Ma",
"Chenglong Bao"
] | Poster | null | Designing an unsupervised image denoising approach in practical applications is a challenging task due to the complicated data acquisition process. In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates th... | [
"Real-world image denoising",
"unsupervised image denoising"
] | null | 1,455 | null | null |
WesiCoRVQ15 | When Optimizing $f$-Divergence is Robust with Label Noise | https://openreview.net/forum?id=WesiCoRVQ15 | [
"Jiaheng Wei",
"Yang Liu"
] | Poster | null | We show when maximizing a properly defined $f$-divergence measure with respect to a classifier's predictions and the supervised labels is robust with label noise. Leveraging its variational form, we derive a nice decoupling property for a family of $f$-divergence measures when label noise presents, where the divergence... | [
"$f-$divergence",
"robustness",
"learning with noisy labels"
] | null | 1,454 | 2011.03687 | title_snapshot |
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