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Gotham City MLXPhysics Workshop

Workshop

Computational Astrophysics

When

  • – ET

  • All Day

  • – ET

Join organizer Shirley Ho for a workshop that brings together scientists working in the field of machine learning and physics for collaboration on applying ML to physics challenges and understanding ML with physics as a tool/insight.

For a detailed workshop schedule click here.

April 29, 2019

Thomas Anglès, Roberto Leonarduzi, Stephani Mallat, Louis Thiry, John Sarka, Sixin Zhang: Multiscale Interactions In Physics and Machine Learning With Deep Nets

April 29, 2019

Nati Srebro: Can You Help Us Understand Deep Learning

April 29, 2019

Short Talks

April 29, 2019

Max Welling: Gauge Fields In Deep Learning

April 30, 2019

Giulio Biroli: From Statistical Physics to Machine Learning and Back

April 30, 2019

Bin Yu: Scientific Machine Learning with Empirical Rigor

April 30, 2019

Giuseppe Carleo: Deep Learning Many-Body Quantum Physics

April 30, 2019

Short Talks/Discussion

April 30, 2019

Florent Krzakala: How Does Gradient Descent Find A Good Minima, Instead of Spurious Ones?

April 30, 2019

Short Talks

April 30, 2019

Eric Vanden-Eijnden: Global Convergence of Neuron Birth-Death Dynamics

April 30, 2019

Learning the Universe With Machine Learning Steps to Open the Pandora Box

April 30, 2019

Kyunghyum Cho: Can a Neural Net Find a New Linguistic Law On Its Own?

April 30, 2019

Barnabas Poczos: Machine Learning for Science Some Open Questions

May 1, 2019

David Blei: Inferring Stardust (with variational inference)

May 1, 2019

Gábor Csányi: Representation and Regression Problems In Molecular Structure and Dynamics

May 1, 2019

Gabriel Peyré: Scaling Optimal Transport to High Dimensional Learning

May 1, 2019

Davi Geiger: Quantum Interference In Computer Vision

May 1, 2019

Bruno Régaldo-Saint Blancard: Statistical Description of the Polarized Interstellar Medium

May 1, 2019

Siamak Ravanbakhsh: Representation Learning With Discrete Symmetries

May 1, 2019

Erwan Allys: Toward Low-dimensional Representation of Physical Fields

May 1, 2019

ML Physics: Examples, Comments, Observations and Questions Part 1

May 1, 2019

ML Physics: Examples, Comments, Observations and Questions Part 2

May 1, 2019

Concluding Remarks

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