3 citations · 6 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
El Mehdi Achour, François Malgouyres, Franck Mamalet
Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve t…
The loss landscape of deep linear neural networks: a second-order analysis
El Mehdi Achour, François Malgouyres, Sébastien Gerchinovitz
We study the optimization landscape of deep linear neural networks with the square loss. It is known that, under weak assumptions, there are no spurious local minima and no local m…