2 papers
cs.LG2022
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Charline Le Lan, Joshua Greaves, Jesse Farebrother +4
Many machine learning problems encode their data as a matrix with a possibly very large number of rows and columns. In several applications like neuroscience, image compression or…
cs.LG2018
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performanc…