171 citations · 357 across the 21 of their papers we have counts for
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cs.CV2023
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization
Juliette Marrie, Michael Arbel, Diane Larlus +1
Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed…
cs.LG2023★ 1 cited
Rethinking Gauss-Newton for learning over-parameterized models
Michael Arbel, Romain Menegaux, Pierre Wolinski
This work studies the global convergence and implicit bias of Gauss Newton's (GN) when optimizing over-parameterized one-hidden layer networks in the mean-field regime. We first es…