Time:

23 May, 10:30-11:30

Abstract: Deep learning, represented by deep artificial neural networks, has dominated numerous important research fields in the past decade. Although deep learning performs excellently in many tasks, it is notoriously a black-box model. A neural network with the widely used ReLU activation is a piecewise linear function over polytopes with a simple functional structure. To enhance the interpretability of deep learning, it is crucial to figure out the properties of polytopes and decipher the functional structure of a ReLU network. In collaboration with leading peer groups from Harbin Institute of Technology, Cornell University, BIGAI, and RIKEN AIP, we dedicate ourselves to answering the following fundamental questions: what kind of functions does a ReLU network learn? And how can we leverage the enhanced understanding to build novel machine learning models? I will share with you our work and discuss issues and opportunities in the field. We welcome interaction with prospective students, postdoctoral fellows, and new collaborators.

腾讯会议：
ID：643-416-163
Code: 520520