4 citations · 4 across the 3 of their papers we have counts for
7 papers
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…
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…
Neuromodulated Learning in Deep Neural Networks
Dennis G Wilson, Sylvain Cussat-Blanc, Hervé Luga +1
In the brain, learning signals change over time and synaptic location, and are applied based on the learning history at the synapse, in the complex process of neuromodulation. Lear…
Positional Cartesian Genetic Programming
DG Wilson, Julian F. Miller, Sylvain Cussat-Blanc +1
Cartesian Genetic Programming (CGP) has many modifications across a variety of implementations, such as recursive connections and node weights. Alternative genetic operators have a…
Evolving Differentiable Gene Regulatory Networks
Dennis G Wilson, Kyle Harrington, Sylvain Cussat-Blanc +1
Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have shown their capacity to solve real-world problems in various domains such as agent control, signal proce…