Why Neural Networks converge to “simple” solutions?


Since 2012, deep neural networks are having an impressive practical success in many domains, yet their theoretical properties are not well understood. I will discuss why does neural network optimization, which based on local greedy steps, tend to converge to:

1) A global minimum, while many local minima exist.

2) A specific “good” global minimum in which the network function is surprisingly “simple” (while many “bad” global minima exist).

Name Daniel Soudry
Date 10-11-19
Faculty  EE
Title  Why Neural Networks converge to “simple” solutions?
Web page https://sites.google.com/site/danielsoudry/
Email daniel.soudry@gmail.com
Study materials About the deep learning era