Guided Learning of Nonconvex Models through Successive Functional
Gradient Optimization
- URL: http://arxiv.org/abs/2006.16840v1
- Date: Tue, 30 Jun 2020 14:31:24 GMT
- Title: Guided Learning of Nonconvex Models through Successive Functional
Gradient Optimization
- Abstract summary: This paper presents a framework for gradient optimization for non functional models such as neural networks.
We provide a theoretical analysis method derived from this framework.
- Score: 22.603803397940133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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