5 citations · 5 across the 3 of their papers we have counts for
3 papers
Leveraging weights signals -- Predicting and improving generalizability in reinforcement learning
Olivier Moulin, Vincent Francois-lavet, Paul Elbers +1
Generalizability of Reinforcement Learning (RL) agents (ability to perform on environments different from the ones they have been trained on) is a key problem as agents have the te…
Hadamard Representation: Scaffolding Performance Across Model-free RL
Jacob E. Kooi, Zhao Yang, Mark Hoogendoorn +1
Deep reinforcement learning agents progressively lose representational capacity during training: neurons become dormant, removing active capacity from the network, and effective ra…
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and…