Deep learning has brought unprecedented success in various tasks ranging from natural language processing, computer vision, to playing strategic games. Nevertheless, deep learning research is mostly guided by empirical observations, and the successful deployment of deep learning technology often requires various heuristics and extensive hyperparameter tuning. In this project, we intend to develop rigorous theories to understand (and possibly solve) various aspects of deep learning including trainability, generalization, and robustness of neural networks.


Bidirectional Self-Normalizing Neural Networks.
Yao Lu, Stephen Gould, and Thalaiyasingam Ajanthan.
arxiv:2006.12169, June 2020.
[pdf] [arxiv] [talk] [bib]