Charged Point Normalization: An Efficient Solution to the Saddle Point Problem
International Conference on Learning Representations(2017)
摘要
Recently, the problem of local minima in very high dimensional non-convex optimization has been challenged and the problem of saddle points has been introduced. This paper introduces a dynamic type of normalization that forces the system to escape saddle points. Unlike other saddle point escaping algorithms, second order information is not utilized, and the system can be trained with an arbitrary gradient descent learner. The system drastically improves learning in a range of deep neural networks on various data-sets in comparison to non-CPN neural networks.
更多查看译文
关键词
point normalization,saddle point problem,efficient solution
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络