In appropriate frameworks, automatic differentiation is transparent to t...
We leverage path differentiability and a recent result on nonsmooth impl...
We provide a simple model to estimate the computational costs of the bac...
Differentiation along algorithms, i.e., piggyback propagation of derivat...
In theory, the choice of ReLU'(0) in [0, 1] for a neural network has a
n...
In view of training increasingly complex learning architectures, we esta...
In view of a direct and simple improvement of vanilla SGD, this paper
pr...
We present a new algorithm to solve min-max or min-min problems out of t...
Automatic differentiation, as implemented today, does not have a simple
...
We consider the long-term dynamics of the vanishing stepsize subgradient...
We provide a lower bound showing that the O(1/k) convergence rate of the...
The Clarke subdifferential is not suited to tackle nonsmooth deep learni...
We devise a learning algorithm for possibly nonsmooth deep neural networ...
We focus on nonconvex and nonsmooth minimization problems with a composi...