When Neurons Fail - Technical Report

7 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Hidden Vulnerability of Distributed Learning in Byzantium (2018, methodology): “In the mean time, theoretical evaluations of this propagated error were derived with a tight bound (El Mhamdi & Guerraoui, 2016; 2017; El Mhamdi et al., 2017) which relies on computing the Lipschitz coefﬁcient of the neural network and proving its exponential dependency with the depth.”
On the Robustness of a Neural Network (2017, background): “In the case where the activation function is bounded, worst-case theoretical analysis reveals [36] 9 that the robustness of the network is completely encompassed in the weight distribution and the network topology: a network of L layers with a K - Lipschitz activation function tolerates the crash…”