41 citations · 64 across the 6 of their papers we have counts for
6 papers
Can Scale Save Us From Plasticity Loss in Large Language Models?
J. Fernando Hernandez-Garcia, Tomás Figliolia, Beren Millidge
The loss of plasticity - the ability of a network to learn new information after having already learned older information - is a fundamental challenge in creating artificial neural…
Reinitializing weights vs units for maintaining plasticity in neural networks
J. Fernando Hernandez-Garcia, Shibhansh Dohare, Jun Luo +1
Loss of plasticity is a phenomenon in which a neural network loses its ability to learn when trained for an extended time on non-stationary data. It is a crucial problem to overcom…
Maintaining Plasticity in Deep Continual Learning
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman +2
Modern deep-learning systems are specialized to problem settings in which training occurs once and then never again, as opposed to continual-learning settings in which training occ…
Learning Sparse Representations Incrementally in Deep Reinforcement Learning
J. Fernando Hernandez-Garcia, Richard S. Sutton
Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumu…
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target
J. Fernando Hernandez-Garcia, Richard S. Sutton
Multi-step methods such as Retrace() and -step -learning have become a crucial component of modern deep reinforcement learning agents. These methods are often evaluated as…
Multi-step Reinforcement Learning: A Unifying Algorithm
Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland +1
Unifying seemingly disparate algorithmic ideas to produce better performing algorithms has been a longstanding goal in reinforcement learning. As a primary example, TD() elegant…