Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

ICML 2020

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Jul 12, 2020
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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. Speakers: Rie Johnson, Tong Zhang

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