Papers
Visual Interaction Networks: Learning a Physics Simulator from Video
Nicholas Watters, Daniel Zoran, Theophane Weber et al.
Controllable Invariance through Adversarial Feature Learning
Qizhe Xie, Zihang Dai, Yulun Du et al.
Learning Active Learning from Data
Ksenia Konyushkova, Raphael Sznitman, Pascal Fua
Neural system identification for large populations separating “what” and “where”
David Klindt, Alexander S Ecker, Thomas Euler et al.
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Roel Dobbe, David Fridovich-Keil, Claire Tomlin
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Zeyuan Allen-Zhu, Elad Hazan, Wei Hu et al.
Nonbacktracking Bounds on the Influence in Independent Cascade Models
Emmanuel Abbe, Sanjeev Kulkarni, Eun Jee Lee
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg, Su-In Lee
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner et al.
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
Wei Wen, Cong Xu, Feng Yan et al.
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
Simon S Du, Yining Wang, Aarti Singh
Matrix Norm Estimation from a Few Entries
Ashish Khetan, Sewoong Oh
Adaptive stimulus selection for optimizing neural population responses
Benjamin Cowley, Ryan Williamson, Katerina Clemens et al.
Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky et al.
Mapping distinct timescales of functional interactions among brain networks
Mali Sundaresan, Arshed Nabeel, Devarajan Sridharan
Revenue Optimization with Approximate Bid Predictions
Andres Munoz, Sergei Vassilvitskii
Deep Learning with Topological Signatures
Christoph Hofer, Roland Kwitt, Marc Niethammer et al.
Triple Generative Adversarial Nets
Chongxuan LI, Taufik Xu, Jun Zhu et al.
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Katrina Ligett, Seth Neel, Aaron Roth et al.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Eirikur Agustsson, Fabian Mentzer, Michael Tschannen et al.
Unsupervised Transformation Learning via Convex Relaxations
Tatsunori B Hashimoto, Percy Liang, John C. Duchi
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
Chris Metzler, Ali Mousavi, Richard Baraniuk
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
Yasin Abbasi Yadkori, Peter L Bartlett, Victor Gabillon
Multiresolution Kernel Approximation for Gaussian Process Regression
Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler