Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning. Our three-day workshop stems on what we identify as the current main bottleneck: understanding the geometrical structure of deep neural networks. This problem is at the confluence of mathematics, computer science, and practical machine learning. We invite the leaders in these fields to bolster new collaborations and to look for new angles of attack on the mysteries of deep learning.
Peter Bartlett, University of California at Berkeley
Leon Bottou, Facebook
Anna Gilbert, University of Michigan
Piotr Indyk, MIT
S. T. Yau, Harvard
Program Committee members