3 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 3 cited
The Theoretical Expressiveness of Maxpooling
Kyle Matoba, Nikolaos Dimitriadis, François Fleuret
Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest…
cs.LG2021★ 2 cited
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
David Lindner, Kyle Matoba, Alexander Meulemans
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes.…