Conference on the Mathematical Theory of Deep Neural Networks

The Conference on the Mathematical Theory of Deep Neural Networks focuses on the recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, which have made DNNs a powerful, versatile method used widely in both machine learning and neuroscience. These advances in practical results, however, have far outpaced a formal understanding of these networks and their training. The purpose of this conference is to give visibility to these results, and those that will follow in their wake, to shed light on the properties of large, adaptive, distributed learning architectures, and to revolutionize our understanding of these systems.