AI in Science & Technology

💠 CALL FOR PAPERS

❇️ Machine Learning and the Physical Sciences Workshop at the 34th
Conference on Neural Information Processing Systems (NeurIPS) December
11, 2020 NeurIPS 2020 is a Virtual-only Conference
https://ml4physicalsciences.github.io/

ABOUT

Machine learning methods have had great success in learning complex
representations of data that enable novel modeling and data processing
approaches in many scientific disciplines. Physical sciences span
problems and challenges at all scales in the universe: from finding
exoplanets in trillions of sky pixels, to developing solutions to the
quantum many-body problem and combinatorial problems, 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, computer vision, sequence modeling, causal reasoning,
generative modeling, and probabilistic inference are critical for
furthering scientific discovery in these and many other areas. 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 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 short papers
(extended abstracts) that demonstrate cutting-edge progress in the
application of machine learning techniques to real-world problems in the
physical sciences and/or using physical insights to understand and
improve machine learning techniques.

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 inference
* 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 short papers (extended abstracts)
will be made available on the workshop website. This does not constitute
an archival publication or formal proceedings; authors retain full
copyright of their work and are free to publish their extended work in
another journal or conference. We allow submission of extended abstracts
that overlap with papers that are under review or have been recently
published in a conference or a journal. However, we do not accept cross
submissions of the same extended abstract to multiple workshops at
NeurIPS. 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.

Accepted work will be presented as posters during the workshop. Each
accepted work entering the poster sessions would have an accompanying
pre-recorded 5-minute video. Please note that at least one coauthor of
each accepted paper will be expected to have a NeurIPS c

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