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- W2958888037 abstract "Inspired by dynamic programming, we propose Stochastic Virtual Gradient Descent (SVGD) algorithm where the Virtual Gradient is defined by computational graph and automatic differentiation. The method is computationally efficient and has little memory requirements. We also analyze the theoretical convergence properties and implementation of the algorithm. Experimental results on multiple datasets and network models show that SVGD has advantages over other stochastic optimization methods." @default.
- W2958888037 created "2019-07-23" @default.
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- W2958888037 date "2019-07-09" @default.
- W2958888037 modified "2023-09-23" @default.
- W2958888037 title "SVGD: A Virtual Gradients Descent Method for Stochastic Optimization" @default.
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- W2958888037 doi "https://doi.org/10.48550/arxiv.1907.04021" @default.
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