Survey of Emerging Theories Explaining Deep Learning Success

The newsletter compiles recent attempts to explain deep learning’s performance. It outlines statistical learning theory as one foundational perspective. The piece highlights the information bottleneck hypothesis as another approach. It discusses the role of overparameterization in shaping model behavior. The author contrasts gradient descent dynamics with representational theory. Empirical evidence supporting each theory is summarized. The article notes gaps where existing theories fall short. It concludes with suggestions for future research directions.