35 citations · 81 across the 22 of their papers we have counts for
3 papers · 1 filter
SGD with Large Step Sizes Learns Sparse Features
Maksym Andriushchenko, Aditya Varre, Loucas Pillaud-Vivien +1
We showcase important features of the dynamics of the Stochastic Gradient Descent (SGD) in the training of neural networks. We present empirical observations that commonly used lar…
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation
Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion
Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the rol…
Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion
The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its su…