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- W4364297091 abstract "In this work, we consider the target of solving the nonlinear and nonconvex optimization problems arising in the training of deep neural networks. To this aim we propose a nonmonotone trust-region (NTR) approach in a stochastic setting under inexact function and gradient approximations. We use the limited memory SR1 (L-SR1) updates as Hessian approximations when the curvature information is obtained by several different strategies. We provide results showing the performance of the proposed optimizer in the training of residual networks for image classification. Our results show that the proposed algorithm provides comparable or better testing accuracy than standard stochastic trust-region depending on the adopted curvature computing strategy and outperforms the well-known Adam optimizer." @default.
- W4364297091 created "2023-04-12" @default.
- W4364297091 creator A5020055228 @default.
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- W4364297091 date "2022-10-01" @default.
- W4364297091 modified "2023-09-27" @default.
- W4364297091 title "A stochastic nonmonotone trust-region training algorithm for image classification" @default.
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- W4364297091 doi "https://doi.org/10.1109/sitis57111.2022.00084" @default.
- W4364297091 hasPublicationYear "2022" @default.
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