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...
Үндсэн зохиолчид: | Berrada, L, Zisserman, A, Kumar, MP |
---|---|
Формат: | Internet publication |
Хэл сонгох: | English |
Хэвлэсэн: |
arXiv
2018
|
Ижил төстэй зүйлс
Ижил төстэй зүйлс
-
Deep Frank-Wolfe for neural network optimization
-н: Berrada, L, зэрэг
Хэвлэсэн: (2019) -
Training neural networks for and by interpolation
-н: Berrada, L, зэрэг
Хэвлэсэн: (2020) -
Riemannian Optimization via Frank-Wolfe Methods
-н: Weber, Melanie, зэрэг
Хэвлэсэн: (2022) -
Riemannian optimization via Frank-Wolfe methods
-н: Weber, M, зэрэг
Хэвлэсэн: (2022) -
New analysis and results for the Frank–Wolfe method
-н: Freund, Robert Michael, зэрэг
Хэвлэсэн: (2016)