471 citations · 729 across the 4 of their papers we have counts for
5 papers
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler +1
Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this go…
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Shun Liao +2
In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend th…
The Reversible Residual Network: Backpropagation Without Storing Activations
Aidan N. Gomez, Mengye Ren, Raquel Urtasun +1
Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. H…
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Roger B. Grosse, Siddharth Ancha, Daniel M. Roy
Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This c…
Exploiting compositionality to explore a large space of model structures
Roger Grosse, Ruslan R Salakhutdinov, William T. Freeman +1
The recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset. We investigate this que…