Overview
The upcoming workshop on Machine Learning and the Physical Sciences will take place at the 33rd Conference on Neural Information Processing Systems (NeurIPS) in December 2019. This event aims to unite experts from computer science, mathematics, and physical sciences to explore the intersection of machine learning and various physical challenges. The workshop will highlight the importance of machine learning in advancing scientific discovery and understanding complex physical phenomena.
Background & Relevance
Machine learning has revolutionized data analysis across numerous scientific fields, particularly in the physical sciences. This area encompasses a wide range of challenges, from detecting exoplanets to analyzing data from high-energy physics experiments. The integration of machine learning techniques is crucial for tackling data-intensive tasks such as anomaly detection, generative modeling, and causal inference. As these methods evolve, understanding their interpretability becomes increasingly vital, allowing researchers to glean insights from the models they develop.
Key Details
- Workshop Dates: December 13 or 14, 2019
- Location: Vancouver Convention Centre, Vancouver, BC, Canada
- Submission Deadline: September 9, 2019, 23:59 PDT
- Author Notification: October 1, 2019
- Camera-Ready Paper Deadline: November 1, 2019
- Workshop Website: Machine Learning and the Physical Sciences
- Submissions Page: EasyChair Submission
Eligibility & Participation
This workshop is open to researchers and practitioners interested in applying machine learning to physical science problems. It targets those working on both completed projects and high-quality works in progress. Participants are encouraged to submit extended abstracts that showcase innovative applications of machine learning in the physical sciences.
Submission or Application Guidelines
- Submissions must be anonymized extended abstracts in PDF format, limited to 4 pages, following the NeurIPS style.
- References do not count towards the page limit.
- Appendices are discouraged, and reviewers will focus on the first four pages.
- At least one co-author from each accepted submission must attend the workshop to present their work.
Additional Context / Real-World Relevance
The workshop seeks to foster interdisciplinary collaboration between machine learning researchers and physical scientists. By sharing insights and methodologies, participants can address significant open problems in the physical sciences. The event will also feature invited talks from leading experts, providing a platform for discussing state-of-the-art techniques and future directions in the field.
Conclusion
This workshop represents a unique opportunity for researchers to engage with peers and contribute to the growing dialogue between machine learning and the physical sciences. Interested parties are encouraged to submit their work and participate in this exciting event at NeurIPS 2019.
Category: CFP & Deadlines
Tags: machine learning, physical sciences, neurips, generative models, model interpretability, probabilistic models, data science, quantum computing
