Physics ∩ ML Workshop (Day 2): Short Talks and Discussions

Опубликовано 24 мая 2019, 20:06

The goal of Physics ∩ ML is to bring together researchers from machine learning and physics to learn from each other and push research forward together. In this inaugural edition, we will especially highlight some amazing progress made in string theory with machine learning and in the understanding of deep learning from a physical angle. Nevertheless, we invite a cast with wide ranging expertise in order to spark new ideas. Plenary sessions from experts in each field and shorter specialized talks will introduce existing research. We will hold moderated discussions and breakout groups in which participants can identify problems and hopefully begin new collaborations in both directions. For example, physical insights can motivate advanced algorithms in machine learning, and analysis of geometric and topological datasets with machine learning can yield critical new insights in fundamental physics.

10:15 AM–11:35 AM
Short talks

Bypassing expensive steps in computational geometry
Yang-Hui He

Learning string theory at Large N
Cody Long

Training machines to extrapolate reliably over astronomical scales
Brent Nelson

Q&A

Breaking the tunnel vision with ML
Sergei Gukov

Can machine learning give us new theoretical insights in physics and math?
Washington Taylor

Brief overview of machine learning holography
Yi-Zhuang You

Q&A

Applications of persistent homology to physics
Alex Cole

Seeking a connection between the string landscape and particle physics
Patrick Vaudrevange

PBs^-1 to science: novel approaches on real-time processing from LHCb at CERN
Themis Bowcock

Q&A

From non-parametric to parametric: manifold coordinates with physical meaning
Marina Meila

Machine learning in quantum many-body physics: A blitz
Yichen Huang

Knot Machine Learning
Vishnu Jejjala

11:35 AM–12:30 PM
Panel discussion with panelists Michael Freedman, Clement Hongler, Gary Shiu, Paul Smolensky, Washington Taylor

See more at microsoft.com/en-us/research/e...