CALL FOR PAPERS

Machine Learning and the Physical Sciences
Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS)
December 13 or 14, 2019
Vancouver Convention Centre, Vancouver, BC, Canada

https://ml4physicalsciences.github.io/


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.


SCOPE

We invite researchers to submit papers in the following and related areas:
* Application of machine 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. We discourage work submitted to other NeurIPS 2019 workshops. 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 will be peer-reviewed in a double-blind setting. 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.

Examples of accepted abstracts from previous years can be found here: https://dl4physicalsciences.github.io/

Submission page: TBA (please check the website https://ml4physicalsciences.github.io for latest information)


CONFIRMED SPEAKERS

Alan Aspuru-Guzik (University of Toronto)
Yasaman Bahri (Google Brain)
Katie Bouman (California Institute of Technology)
Lenka Zdeborova (Institut de Physique Theorique)
(More to be confirmed)

ORGANIZERS

Atilim Gunes Baydin (University of Oxford)
Juan Felipe Carrasquilla (Vector Institute / University of Waterloo)
Shirley Ho (Flatiron Institute / Princeton University)
Karthik Kashinath (NERSC, Berkeley Lab)
Michela Paganini (Yale University)
Savannah Thais (Yale University)

STEERING COMMITTEE

Anima Anandkumar (California Institute of Technology / NVIDIA)
Kyle Cranmer (New York University)
Roger Melko (University of Waterloo)
Prabhat (NERSC, Berkeley Lab)
Frank Wood (University of British Columbia)


IMPORTANT DATES (TENTATIVE)

* 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


REGISTRATION

Participants should refer to the NeurIPS 2019 website (https://neurips.cc/) for information on how to register for the workshop.


CONTACT

Please direct all questions and comments to Atilim Gunes Baydin (gunes@robots.ox.ac.uk). Please include “[ML4PS NeurIPS 2019]” in the subject line.