4 citations · 6 across the 4 of their papers we have counts for
3 papers · 1 filter
Offline Policy Comparison under Limited Historical Agent-Environment Interactions
Anton Dereventsov, Joseph D. Daws, Clayton Webster
We address the challenge of policy evaluation in real-world applications of reinforcement learning systems where the available historical data is limited due to ethical, practical,…
Neural network integral representations with the ReLU activation function
Armenak Petrosyan, Anton Dereventsov, Clayton Webster
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function. We assume that the outer weighs admit a finite $L…
Robust learning with implicit residual networks
Viktor Reshniak, Clayton Webster
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outp…