Papers
11,015 papers found
SMASH: One-Shot Model Architecture Search through HyperNetworks
Andrew Brock, Theo Lim, J.M. Ritchie et al.
Smooth Loss Functions for Deep Top-k Classification
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu et al.
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Feiwen Zhu, Jeff Pool, Michael Andersch et al.
Spatially Transformed Adversarial Examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li et al.
SpectralNet: Spectral Clustering using Deep Neural Networks
Uri Shaham, Kelly Stanton, Henry Li et al.
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama et al.
Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Köhler et al.
Stabilizing Adversarial Nets with Prediction Methods
Abhay Yadav, Sohil Shah, Zheng Xu et al.
Stochastic Activation Pruning for Robust Adversarial Defense
Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton et al.
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari, Stefano Soatto
Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan et al.
Syntax-Directed Variational Autoencoder for Structured Data
Hanjun Dai, Yingtao Tian, Bo Dai et al.
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
Manuel Molano-Mazon, Arno Onken, Eugenio Piasini* et al.
Synthetic and Natural Noise Both Break Neural Machine Translation
Yonatan Belinkov, Yonatan Bisk
TD or not TD: Analyzing the Role of Temporal Differencing in Deep Reinforcement Learning
Artemij Amiranashvili, Alexey Dosovitskiy, Vladlen Koltun et al.
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong*, Shixiang Gu*, Murtaza Dalal et al.
Temporally Efficient Deep Learning with Spikes
Peter O'Connor, Efstratios Gavves, Matthias Reisser et al.
The High-Dimensional Geometry of Binary Neural Networks
Alexander G. Anderson, Cory P. Berg
The Implicit Bias of Gradient Descent on Separable Data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson et al.
The Kanerva Machine: A Generative Distributed Memory
Yan Wu, Greg Wayne, Alex Graves et al.
The power of deeper networks for expressing natural functions
David Rolnick, Max Tegmark
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning
Audrunas Gruslys, Will Dabney, Mohammad Gheshlaghi Azar et al.
Thermometer Encoding: One Hot Way To Resist Adversarial Examples
Jacob Buckman, Aurko Roy, Colin Raffel et al.
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
Tomer Galanti, Lior Wolf, Sagie Benaim