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- W3039487613 abstract "This paper presents a framework of successive functional gradient optimization for training nonconvex models such as neural networks, where training is driven by mirror descent in a function space. We provide a theoretical analysis and empirical study of the training method derived from this framework. It is shown that the method leads to better performance than that of standard training techniques." @default.
- W3039487613 created "2020-07-10" @default.
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- W3039487613 date "2020-06-30" @default.
- W3039487613 modified "2023-09-26" @default.
- W3039487613 title "Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization" @default.
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