Poster

Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization

Rie Johnson · Tong Zhang

Virtual

Keywords: [ Optimization ] [ Algorithms ] [ Deep Learning - Algorithms ]

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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