49 citations · 61 across the 8 of their papers we have counts for
4 papers · 1 filter
On Neural Consolidation for Transfer in Reinforcement Learning
Valentin Guillet, Dennis G. Wilson, Carlos Aguilar-Melchor +1
Although transfer learning is considered to be a milestone in deep reinforcement learning, the mechanisms behind it are still poorly understood. In particular, predicting if knowle…
Equivariance-aware Architectural Optimization of Neural Networks
Kaitlin Maile, Dennis G. Wilson, Patrick Forré
Incorporating equivariance to symmetry groups as a constraint during neural network training can improve performance and generalization for tasks exhibiting those symmetries, but s…
When, where, and how to add new neurons to ANNs
Kaitlin Maile, Emmanuel Rachelson, Hervé Luga +1
Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning. By decomposing it into triggers and initializations…
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS
Kaitlin Maile, Erwan Lecarpentier, Hervé Luga +1
Differentiable Architecture Search (DARTS) is a recent neural architecture search (NAS) method based on a differentiable relaxation. Due to its success, numerous variants analyzing…