Deep Frank-Wolfe for neural network optimization
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...
| Main Authors: | , , |
|---|---|
| Format: | Internet publication |
| Language: | English |
| Published: |
arXiv
2018
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