2024
Recent advances in deep neural networks (DNNs), combined with open, easily-accessible implementations, 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 dearth of rigorous analysis for these techniques limits their usefulness in addressing scientific questions and, more broadly, hinders systematic design of the next generation of networks. Recently, long-past-due theoretical results have begun to emerge from researchers in a number of fields. 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.
Important Dates
Important Dates
Submissions open:
Submissions open:
Notifications of decision sent:
Notifications of decision sent:
Registration opens:
Registration opens:
Registration deadline:
Registration deadline:
Conference:
Conference:
July 19, 2024
July 19, 2024
Oct 8, 2024
Oct 8, 2024
Oct 8, 2024
Oct 8, 2024
Nov 15, 2024
Nov 15, 2024
Nov 15-16, 2024
Nov 15-16, 2024
Submissions close:
Submissions close:
Sep 14, 2024
Sep 14, 2024
2024 Speakers
2024 Speakers
John Duchi
John Duchi
Stanford University
Stanford University
Melanie Weber
Melanie Weber
Harvard University
Harvard University
Cynthia Rudin
Cynthia Rudin
Duke University
Duke University
Boris Hanin
Boris Hanin
Princeton University
Princeton University
Qing Qu
Qing Qu
University of Michigan
University of Michigan
Virginia Smith
Virginia Smith
Carnegie Mellon University
Carnegie Mellon University