Machine learning is undergoing a scientific revolution, with a succession of experimental triumphs. These empirical successes have often led to, and sometimes been inspired by, improved theoretical understanding that leans heavily on insight from physics. This Aspen Winter Conference will investigate the use of ideas from theoretical physics --- in particular, high energy theory, condensed matter theory, and statistical mechanics --- to better understand machine learning. We will bring together researchers from the theoretical physics and machine learning communities to discuss the physics of ML, with an eye towards both improved performance and progress on new challenges.
For the 2019 version of this conference, see here
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Adam Brown
Ethan Dyer
Paul Ginsparg
Guy Gur-Ari
Maithra Raghu