no code implementations • 11 Aug 2023 • Andrea Gesmundo, Kaitlin Maile
Training state-of-the-art neural networks requires a high cost in terms of compute and time.
no code implementations • 11 Oct 2022 • 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 such symmetries are often not perfectly nor explicitly present.
1 code implementation • 17 Feb 2022 • Kaitlin Maile, Emmanuel Rachelson, Hervé Luga, Dennis G. Wilson
Neurogenesis in ANNs is an understudied and difficult problem, even compared to other forms of structural learning like pruning.
no code implementations • 22 Jun 2021 • Kaitlin Maile, Erwan Lecarpentier, Hervé Luga, Dennis G. Wilson
Differentiable Architecture Search (DARTS) is a recent neural architecture search (NAS) method based on a differentiable relaxation.
Ranked #10 on Neural Architecture Search on CIFAR-100