Deep Learning
Improving Neural Program Synthesis with Inferred Execution Traces
Eui Chul Shin, Illia Polosukhin, Dawn Song
Neural Information Processing Systems http://nips.cc/
Automatic differentiation in ML: Where we are and where we should be going
Bart van Merrienboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin
Firstly, many machine learning models use optimization algorithms which require access to derivatives of the model.
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski
Neural Information Processing Systems http://nips.cc/
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang
Besides, we observe that existing works still cannotdirectlypropagate the gradient information from deep layers to shallow layers.
Transfer of Deep Reactive Policies for MDP Planning
Aniket (Nick) Bajpai, Sankalp Garg, None
Neural Information Processing Systems http://nips.cc/
Reconciling ฮป-Returns with Experience Replay
Brett Daley, Christopher Amato
A unique benefit to this approach is that each transition's TD error can be
Hierarchically Gated Recurrent Neural Network for Sequence Modeling
Recently, there has been a renewed interest in using linear RNNs for efficient sequence modeling.