资源分类
》 资源 》论文》Deep learning via Hessian-free optimization

Deep learning via Hessian-free optimization

2020-02-26 | |  57 |   48 |   0

Abstract

We develop a 图片.png optimization method based on the “Hessian-free” approach, and apply it to training deep auto-encoders. Without using pre-training, we obtain results superior to those reported by Hinton & Salakhutdinov (2006) on the same tasks they considered. Our method is practical, easy to use, scales nicely to very large datasets, and isn’t limited in applicability to aut encoders, or any specific model class. We also discuss the issue of “pathological curvature” as a possible explanation for the difficulty of deeplearning and how 图片.png optimization, and our method in particular, effectively deals with it.

上一篇：3D Convolutional Neural Networks for Human Action Recognition

下一篇：Analysis of a Classification-based Policy Iteration Algorithm

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...