Deep Frank-Wolfe for neural network optimization

Deep Frank-Wolfe for neural network optimization

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Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms. The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm...

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Dettagli Bibliografici
Autori principali:	Berrada, L, Zisserman, A, Kumar, MP
Natura:	Internet publication
Lingua:	English
Pubblicazione:	arXiv 2018

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