Papers
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj et al.
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan et al.
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
Accelerated Spectral Ranking
Arpit Agarwal, Prathamesh Patil, Shivani Agarwal
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni
Accelerating Natural Gradient with Higher-Order Invariance
Yang Song, Jiaming Song, Stefano Ermon
Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis et al.
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar, Ludwig Schmidt, Aleksander Madry
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Alp Yurtsever, Olivier Fercoq, Francesco Locatello et al.
Active Learning with Logged Data
Songbai Yan, Kamalika Chaudhuri, Tara Javidi
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen, Deva Ramanan, Charless Fowlkes
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
Huasen Wu, Xueying Guo, Xin Liu
Adaptive Sampled Softmax with Kernel Based Sampling
Guy Blanc, Steffen Rendle
Adaptive Three Operator Splitting
Fabian Pedregosa, Gauthier Gidel
Addressing Function Approximation Error in Actor-Critic Methods
Scott Fujimoto, Herke Hoof, David Meger
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick et al.
A Distributed Second-Order Algorithm You Can Trust
Celestine Duenner, Aurelien Lucchi, Matilde Gargiani et al.
ADMM and Accelerated ADMM as Continuous Dynamical Systems
Guilherme Franca, Daniel Robinson, Rene Vidal
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian et al.
Adversarial Distillation of Bayesian Neural Network Posteriors
Kuan-Chieh Wang, Paul Vicol, James Lucas et al.
Adversarial Learning with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu et al.
Adversarially Regularized Autoencoders
Junbo Zhao, Yoon Kim, Kelly Zhang et al.
Adversarial Regression with Multiple Learners
Liang Tong, Sixie Yu, Scott Alfeld et al.