About

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, to predicting how extreme weather events will vary with climate change. Tackling a number of associated data-intensive tasks including, but not limited to, segmentation, 3D computer vision, sequence modeling, causal reasoning, generative modeling, 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 aim to bring together computer scientists, mathematicians and physical scientists who are interested in applying machine learning to various outstanding physical problems, including in inverse problems; approximating physical processes; understanding what a learned model really represents; and connecting tools and insights from the 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 the physical sciences, and using physical insights to understand what the learned model represents.

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 the production of new approaches to solving challenging open problems in the sciences. Invited talks from leading individuals in both communities will cover the state-of-the-art techniques and set the stage for this workshop.

NeurIPS 2019

The Machine Learning and the Physical Sciences 2019 workshop will be held on December 13 or 14, 2019 as a part of the 33rd Annual Conference on Neural Information Processing Systems, at the Vancouver Convention Center, Vancouver, Canada. Please check the main conference website for information about registration, schedule, venue, and travel arrangements.

Schedule

Invited speakers

To come.

Schedule

To come.

Call for papers

We invite researchers to submit papers particularly in the following and related areas:

  • Application of machine and deep learning to physical sciences
  • Generative models
  • Likelihood-free inference
  • Variational inference
  • Simulation-based models
  • Implicit models
  • Probabilistic models
  • Model interpretability
  • Approximate Bayesian computation
  • Strategies for incorporating prior scientific knowledge into machine learning algorithms
  • Experimental design
  • Any other area related to the subject of the workshop

Submissions of completed projects as well as high-quality works in progress are welcome. All accepted papers will be made available on the workshop website and presented as posters or contributed talks during the workshop. As this does not constitute an archival publication or formal proceedings, authors are free to publish their extended work elsewhere. Submissions will be kept confidential until they are accepted and authors confirm that they can be included in the workshop. If a submission is not accepted, or withdrawn for any reason, it will be kept confidential and not made public.

Submissions should be anonymized short papers up to 4 pages in PDF format, typeset using the NeurIPS style. References do not count towards the page limit. Appendices are discouraged, and reviewers are not expected to read beyond the first 4 pages. A workshop-specific modified NeurIPS style file will be provided for the camera-ready versions, after the author notification date.

Accepted submissions will be presented as posters during the workshop. Several accepted submissions will be selected for contributed talks.

Submission instructions

To come.

Travel support and complimentary registration

To come.

Important dates

  • Submission deadline: September 9, 2019, 23:59 PDT
  • Author notification: October 1, 2019
  • Camera-ready (final) paper deadline: November 1, 2019
  • Workshop: December 13 or 14, 2019

Organizers

Steering Committee

Sponsors

To come.

Location

Vancouver Convention Centre, 1055 Canada Pl, Vancouver, BC, V6C 0C3, Canada