When Neurons Fail

We view a neural network as a distributed system of which neurons can fail independently, and we evaluate its robustness in the absence of any (recovery) learning phase. We give tight bounds on the number of neurons that can fail without harming the result of a computation. To determine our bounds,...

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Veröffentlicht in:	arXiv.org 2017-06
Hauptverfasser:	El Mahdi El Mhamdi, Guerraoui, Rachid
Format:	Artikel
Sprache:	eng
Schlagworte:	Combinatorial analysis Computer networks Computer Science - Distributed, Parallel, and Cluster Computing Computer Science - Neural and Evolutionary Computing Continuity (mathematics) Neural networks Neurons Quantitative Biology - Neurons and Cognition Robustness Statistics - Machine Learning Synapses Topology
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