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cs.LG2022
Finite Sample Identification of Wide Shallow Neural Networks with Biases
Massimo Fornasier, Timo Klock, Marco Mondelli +1
Artificial neural networks are functions depending on a finite number of parameters typically encoded as weights and biases. The identification of the parameters of the network fro…
cs.LG2021
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
Christian Fiedler, Massimo Fornasier, Timo Klock +1
In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a fi…
cs.LG2019★ 2 cited
Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks
Massimo Fornasier, Timo Klock, Michael Rauchensteiner
We address the structure identification and the uniform approximation of two fully nonlinear layer neural networks of the type on from a sm…