Ponente: Xavier Fernández-Real (École polytechnique fédérale de Lausanne ‐ EPFL)
Título: "The continuous formulation of shallow neural networks as Wasserstein-type gradient flows"
Abstract: It has been recently observed that the training of a single hidden layer artificial neural network can be reinterpreted as a Wasserstein gradient flow for the weights for the error functional. In the limit, as the number of parameters tends to infinity, this gives rise to a family of parabolic equations. This talk aims to discuss this relation, focusing on the associated theoretical aspects appealing to the mathematical community and providing a list of interesting open problems.