Papers
Eigen-Distortions of Hierarchical Representations
Alexander Berardino, Valero Laparra, Johannes Ballé et al.
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
Surbhi Goel, Adam Klivans
Elementary Symmetric Polynomials for Optimal Experimental Design
Zelda E. Mariet, Suvrit Sra
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Yuandong Tian, Qucheng Gong, Wenling Shang et al.
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Serhii Havrylov, Ivan Titov
End-to-end Differentiable Proving
Tim Rocktäschel, Sebastian Riedel
Ensemble Sampling
Xiuyuan Lu, Benjamin Van Roy
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Emmanouil Platanios, Hoifung Poon, Tom M. Mitchell et al.
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein’s Lemma
Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang et al.
Estimating Mutual Information for Discrete-Continuous Mixtures
Weihao Gao, Sreeram Kannan, Sewoong Oh et al.
Estimation of the covariance structure of heavy-tailed distributions
Xiaohan Wei, Stanislav Minsker
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning
Justin Fu, John Co-Reyes, Sergey Levine
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models
Rishit Sheth, Roni Khardon
Expectation Propagation for t-Exponential Family Using q-Algebra
Futoshi Futami, Issei Sato, Masashi Sugiyama
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Le Fang, Fan Yang, Wen Dong et al.
Experimental Design for Learning Causal Graphs with Latent Variables
Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Haoran Tang, Rein Houthooft, Davis Foote et al.
Exploring Generalization in Deep Learning
Behnam Neyshabur, Srinadh Bhojanapalli, David Mcallester et al.
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Marcel Nonnenmacher, Srinivas C. Turaga, Jakob H Macke
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Evan Racah, Christopher Beckham, Tegan Maharaj et al.
Fader Networks:Manipulating Images by Sliding Attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier et al.
Fair Clustering Through Fairlets
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi et al.
FALKON: An Optimal Large Scale Kernel Method
Alessandro Rudi, Luigi Carratino, Lorenzo Rosasco
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Artur Speiser, Jinyao Yan, Evan W Archer et al.
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Jeffrey Regier, Michael I Jordan, Jon McAuliffe