Thoughts on today's learning theory - Tomaso Poggio
Tomaso Poggio - MIT
We are delighted to announce the upcoming MaLGa Seminar Series. This event is part of Ellis Genova's activities.
Where and when
Friday 4 February 2022 - 14:00
Room 706, via Dodecaneso 35, Genoa, IT
The live stream will be available on 706DIMA - YouTube
I will describe a personal perspective on the current state of key problems in learning theory. In addition to CNN, several architectures with good performance have emerged, such as MLP transformers, sensors and mixers. Is there a common reason for all of them and their good performance? A natural conjecture is that these modern architectures are good for approximating, learning, and optimizing input-output mappings that can be represented by "sparse" functions that are effectively low-density. In particular, these target functions are typically compositional functions with a function graph that has nodes each with dimensionality at most k, with k << d where d is the dimensionality of the function domain.
Tomaso A. Poggio is the Eugene McDermott Professor in MIT's Department of Cognitive and Brain Sciences and the director of the NSF Center for Brains, Minds and Machines at MIT. He is a founding member of the McGovern Institute and the Computer Science and Artificial Intelligence Laboratory. Former Corporate Fellow of Thinking Machines Corporation, former director of PHZ Capital Partners, Inc. and Mobileye, he has been involved in starting or investing in several other high tech companies including Arris Pharmaceutical, nFX, Imagen, Digital Persona, Deep Mind and Orcam. He is one of the most cited computational scientists and has mentored PhD and postdoc students who are some of the current leaders in intelligence science and engineering.