Saturday December 12th, 2015, 14:00-18:00
Santiago, CHILE
Description
The past five years have seen a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for feature learning and classification. However, the mathematical reasons for this success remain elusive. This tutorial will review recent work that aims to provide a mathematical justification for properties of special classes of deep networks, such as global optimality, invariance, and stability of the learned representations.
Organizers
Joan Bruna, Assistant Professor of Statistics, UC Berkeley
Guillermo Sapiro, Professor of Electrical Engineering, Duke University
René Vidal, Professor of Biomedical Engineering, Johns Hopkins University