Deep Learning for Physics

Seminar Series
Date
Sep 10, 2019Feb 6, 2020
Location
PCTS, Jadwin Hall, Room 407

Details

Event Description

Organizers: Sanjeev Arora, Curtis Callan, and Victor Mikhaylov 

“Deep learning” refers to use of neural networks to solve learning problems, including “learning” hidden structures in large and complex data sets. The theory for this field is still in its infancy. Lately physical and biological scientists have begun to explore how it might apply to their domains. This seminar series seeks to introduce the theoretical science community in Princeton and surrounding regions to the practice, promise, and problems of deep learning. It will consist of monthly afternoon sessions ---geared to the broader scientific community--- that will feature an invited talk followed by informal discussions among participants.

September 10, 2019 – Sanjeev Arora and Pankaj Mehta

October 22, 2019 – Guiseppe Carleo and Or Sharir

November 19, 2019—Kyle Cranmer and James Halverson

February 6, 2020 – Lenka Zdeborova – confirmed but still need title and abstract

March 10, 2020 – Shirley Ho -- Canceled due to Covid-19 Shut Down

Sponsor
PCTS