Exploitation of Contributions to Information Gain, from Learning Analysis to Architecture Synthesis
作者:Bertrand Augereau, Thierry Simon, Jacky Bernard
摘要
We introduce a new analysis tool able to test the learning distribution inside an MLP network. This tool is based on the exploitation of specific indicators which measure the contribution of any interconnection graph subset to the information gain evolution. We present an application of this method to an efficient architecture synthesis algorithm.
论文关键词:Hessian eigenvalues, information gain, learning, neural architectures
论文评审过程:
论文官网地址:https://doi.org/10.1023/A:1009649125520