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- W4310430133 abstract "Deep Learning optimization involves minimizing a high-dimensional loss function in the weight space which is often perceived as difficult due to its inherent difficulties such as saddle points, local minima, ill-conditioning of the Hessian and limited compute resources. In this paper, we provide a comprehensive review of $14$ standard optimization methods successfully used in deep learning research and a theoretical assessment of the difficulties in numerical optimization from the optimization literature." @default.
- W4310430133 created "2022-12-10" @default.
- W4310430133 creator A5036464517 @default.
- W4310430133 date "2022-11-28" @default.
- W4310430133 modified "2023-09-30" @default.
- W4310430133 title "A survey of deep learning optimizers -- first and second order methods" @default.
- W4310430133 doi "https://doi.org/10.48550/arxiv.2211.15596" @default.
- W4310430133 hasPublicationYear "2022" @default.
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