4 citations · 7 across the 11 of their papers we have counts for
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cs.LG2022
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…
cs.LG2022★ 4 cited
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…