Overview
The Machine Learning and Physical Sciences workshop is set to take place during the 33rd Conference on Neural Information Processing Systems (NeurIPS) in December 2019. This workshop aims to unite experts from both machine learning and physical sciences to explore the intersection of these fields. By fostering collaboration, the event seeks to address complex challenges in scientific research through innovative machine learning techniques.
Background & Relevance
Machine learning has revolutionized various scientific disciplines by enabling advanced data processing and modeling capabilities. In the realm of physical sciences, researchers face numerous challenges, from analyzing astronomical data to understanding quantum systems. The integration of machine learning into these areas not only enhances scientific discovery but also provides insights into the interpretability of models. This workshop emphasizes the importance of interdisciplinary dialogue, encouraging contributions that leverage machine learning to solve pressing physical problems.
Key Details
- Event: Machine Learning and the Physical Sciences Workshop
- Date: December 13 or 14, 2019
- Location: Vancouver Convention Centre, Vancouver, BC, Canada
- Website: Workshop Website
- Submission Deadline: September 16, 2019, 23:59 PDT
- Author Notification: October 1, 2019
- Camera-Ready Deadline: November 1, 2019
Eligibility & Participation
This workshop invites researchers from various backgrounds, including computer science, mathematics, and physical sciences, to submit their work. It is particularly aimed at those applying machine learning techniques to tackle significant problems in the physical sciences. Both completed projects and high-quality works in progress are welcome.
Submission or Application Guidelines
Participants are required to submit anonymized extended abstracts of up to four pages in PDF format, formatted according to the NeurIPS style guidelines. References do not count towards the page limit, and appendices are discouraged. Accepted submissions will be presented as posters, with some selected for contributed talks. Authors maintain copyright and can publish their work elsewhere.
For submission, please visit: Submission Page
Additional Context / Real-World Relevance
The workshop’s focus on machine learning applications in physical sciences is timely, given the increasing complexity of scientific data and the need for innovative analytical techniques. By bridging the gap between machine learning and physical sciences, this event aims to inspire new research directions and methodologies that can lead to significant advancements in both fields.
Conclusion
The Machine Learning and Physical Sciences workshop presents a unique opportunity for researchers to showcase their work and engage with experts in the field. Interested participants are encouraged to submit their extended abstracts and contribute to this important dialogue. For further inquiries, please contact Atilim Gunes Baydin at gunes@robots.ox.ac.uk.
Category: CFP & Deadlines
Tags: machine learning, physical sciences, neurips, generative models, model interpretability, data science, poster presentations, conference
