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Program Highlights »
Fri Dec 11 07:00 AM -- 03:15 PM (PST)
Machine Learning and the Physical Sciences
Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Bousso Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais

Workshop Home Page

Machine learning methods have had great success in learning complex representations that enable them to make predictions about unobserved data. Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets in trillions of sky pixels, to finding machine learning inspired solutions to the quantum many-body problem, to detecting anomalies in event streams from the Large Hadron Collider. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, 3D computer vision, sequence modeling, causal reasoning, and efficient probabilistic inference are critical for furthering scientific discovery. In addition to using machine learning models for scientific discovery, the ability to interpret what a model has learned is receiving an increasing amount of attention.

In this targeted workshop, we would like to bring together computer scientists, mathematicians and physical scientists who are interested in applying machine learning to various outstanding physical problems, in particular in inverse problems and approximating physical processes; understanding what the learned model really represents; and connecting tools and insights from physical sciences to the study of machine learning models. In particular, the workshop invites researchers to contribute papers that demonstrate cutting-edge progress in the application of machine learning techniques to real-world problems in physical sciences, and using physical insights to understand what the learned model means.

By bringing together machine learning researchers and physical scientists who apply machine learning, we expect to strengthen the interdisciplinary dialogue, introduce exciting new open problems to the broader community, and stimulate production of new approaches to solving open problems in sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop.

Gather Town link: https://neurips.gather.town/app/GS7AwXNphTXVVEZH/NeurIPS%20ML4PS

Session 1 | Opening remarks (Opening remarks (live))
Session 1 | Invited talk: Lauren Anderson, "3D Milky Way Dust Map using a Scalable Gaussian Process" (Invited talk)
Session 1 | Invited talk Q&A: Lauren Anderson (Q&A (live))
Session 1 | Invited talk: Michael Bronstein, "Geometric Deep Learning for Functional Protein Design" (Invited talk)
Session 1 | Invited talk Q&A: Michael Bronstein (Q&A (live))
Session 1 | Poster session (Poster session (Gather.town))
Session 2 | Opening remarks (Opening remarks (live))
Session 2 | Invited talk: Estelle Inack, "Variational Neural Annealing" (Invited talk)
Session 2 | Invited talk Q&A: Estelle Inack (Q&A (live))
Session 2 | Invited talk: Phiala Shanahan, "Generative Flow Models for Gauge Field Theory" (Invited talk)
Session 2 | Invited talk Q&A: Phiala Shanahan (Q&A (live))
Session 2 | Poster session (Poster session (Gather.town))
Session 3 | Opening remarks (Opening remarks (live))
Session 3 | Invited talk: Laura Waller, "Physics-based Learning for Computational Microscopy" (Invited talk)
Session 3 | Invited talk Q&A: Laura Waller (Q&A (live))
Session 3 | Community development breakouts (Community breakout session (Gather.town))
Session 3 | Feedback from community development breakouts (Feedback remarks (live))