43 citations · 126 across the 29 of their papers we have counts for
3 papers · 1 filter
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk +6
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effe…
Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representat…
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
Craig J. Bester, Steven D. James, George D. Konidaris
Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a framework for solving complex domains that requi…