Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Rie Johnson; Tong Zhang

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arXiv · 2006.16840

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Rie Johnson·Tong Zhang

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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.

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Rie Johnson, Tong Zhang. 2020-06-30. Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. https://arxiv.org/abs/2006.16840

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