1 citations · 1 across the 4 of their papers we have counts for
4 papers
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (P…
Geometry-induced Regularization in Deep ReLU Neural Networks
Joachim Bona-Pellissier, François Malgouyres, François Bachoc
Neural networks with a large number of parameters often do not overfit, owing to implicit regularization that favors \lq good\rq{} networks. Other related and puzzling phenomena in…
Local Identifiability of Deep ReLU Neural Networks: the Theory
Joachim Bona-Pellissier, François Malgouyres, François Bachoc
Is a sample rich enough to determine, at least locally, the parameters of a neural network? To answer this question, we introduce a new local parameterization of a given deep ReLU…
Parameter identifiability of a deep feedforward ReLU neural network
Joachim Bona-Pellissier, François Bachoc, François Malgouyres
The possibility for one to recover the parameters-weights and biases-of a neural network thanks to the knowledge of its function on a subset of the input space can be, depending on…