4 citations · 9 across the 9 of their papers we have counts for
3 papers · 1 filter
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra +3
Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep lear…
On the Number of Regions of Piecewise Linear Neural Networks
Alexis Goujon, Arian Etemadi, Michael Unser
Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is…
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Sebastian Neumayer, Alexis Goujon, Pakshal Bohra +1
Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there…