NeurIPS 2020 - Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems

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