ICML 2020poster9 citations
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization
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.
BibTeX
@InProceedings{pmlr-v119-johnson20b,
title = {Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization},
author = {Johnson, Rie and Zhang, Tong},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {4921--4930},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/johnson20b/johnson20b.pdf},
url = {https://proceedings.mlr.press/v119/johnson20b.html},
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.}
}