NIPS 2017 Workshop on Deep Learning for Physical Sciences (DLPS 2017)
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Atılım Güneş Baydin
Sep 19, 2017, 2:27:47 PM9/19/17
to Machine Learning News
CALL FOR PAPERS
NIPS 2017 Workshop on Deep Learning for Physical Sciences (DLPS 2017)
Friday, December 8, 2017
31st Annual Conference on Neural Information Processing Systems (NIPS)
Long Beach Convention & Entertainment Center, Long Beach, CA, United States
WEBSITE
https://dl4physicalsciences.github.io/
ABSTRACT
Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets and asteroids in trillions of sky-survey pixels, to automatic tracking of extreme weather phenomena in climate datasets, to detecting anomalies in event streams from the Large Hadron Collider at CERN. Tackling a number of associated data-intensive tasks, including, but not limited to, regression, classification, clustering, dimensionality reduction, likelihood-free inference, generative models, and experimental design are critical for furthering scientific discovery. The Deep Learning for Physical Sciences (DLPS) workshop invites researchers to contribute papers that demonstrate progress in the application of machine and deep learning techniques to real-world problems in physical sciences (including the fields and subfields of astronomy, chemistry, Earth science, and physics).
We will discuss research questions, practical implementation challenges, performance / scaling, and unique aspects of processing and analyzing scientific datasets. The target audience comprises members of the machine learning community who are interested in scientific applications and researchers in the physical sciences. By bringing together these two communities, we expect to strengthen dialogue, introduce exciting new open problems to the wider NIPS community, and stimulate production of new approaches to solving science problems. Invited talks from leading individuals from both communities will cover the state-of-the-art techniques and set the stage for this workshop.
SCOPE
We invite researchers to submit papers particularly 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
* 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
All accepted papers will be made available on the workshop website and included in the poster session during the workshop. As this does not constitute an archival publication or formal proceedings, authors are free to publish their extended work elsewhere.
Up to 6 accepted submissions will be selected for 20-minute contributed talks.
IMPORTANT DATES
* Submission deadline: November 1, 2017
* Author notification: November 10, 2017
* NIPS deadline to cancel registration: November 16, 2017
* Camera-ready deadline: December 1, 2017
* Workshop: December 8, 2017
SUBMISSIONS
Submissions should be anonymized short papers up to 4 pages in PDF format, typeset using the NIPS style (https://nips.cc/Conferences/2017/PaperInformation/StyleFiles). References do not count towards the page limit. A workshop-specific modified NIPS style file will be provided for the camera-ready versions, after the author notification date.
Submissions will be handled through the EasyChair website:
https://easychair.org/conferences/?conf=dlps2017
ORGANIZERS
Atilim Gunes Baydin (University of Oxford)
Prabhat (NERSC, Berkeley Lab)
Kyle Cranmer (New York University)
Frank Wood (University of Oxford)
REGISTRATION
Participants should refer to the NIPS 2017 website (https://nips.cc) for information on how to register for the workshop.
As of September 18, 2017, NIPS conference registrations are sold out. A limited number of registrations for workshops and tutorials are still available. We encourage participants to register as soon as possible. NIPS allows the cancellation of registrations by November 16, 2017 with full refund of the registration fee (https://nips.cc/Help/CancellationPolicy). Note that the author notification date for DLPS 2017 is November 10, 2017, to allow cancellation in the case of a submission not accepted.
CONTACT
Please direct all questions and comments to Atilim Gunes Baydin <gu...@robots.ox.ac.uk>. Please include “[DLPS 2017]” in the subject line.
NIPS 2017 Workshop on Deep Learning for Physical Sciences (DLPS 2017)
Friday, December 8, 2017
31st Annual Conference on Neural Information Processing Systems (NIPS)
Long Beach Convention & Entertainment Center, Long Beach, CA, United States
WEBSITE
https://dl4physicalsciences.github.io/
ABSTRACT
Physical sciences span problems and challenges at all scales in the universe: from finding exoplanets and asteroids in trillions of sky-survey pixels, to automatic tracking of extreme weather phenomena in climate datasets, to detecting anomalies in event streams from the Large Hadron Collider at CERN. Tackling a number of associated data-intensive tasks, including, but not limited to, regression, classification, clustering, dimensionality reduction, likelihood-free inference, generative models, and experimental design are critical for furthering scientific discovery. The Deep Learning for Physical Sciences (DLPS) workshop invites researchers to contribute papers that demonstrate progress in the application of machine and deep learning techniques to real-world problems in physical sciences (including the fields and subfields of astronomy, chemistry, Earth science, and physics).
We will discuss research questions, practical implementation challenges, performance / scaling, and unique aspects of processing and analyzing scientific datasets. The target audience comprises members of the machine learning community who are interested in scientific applications and researchers in the physical sciences. By bringing together these two communities, we expect to strengthen dialogue, introduce exciting new open problems to the wider NIPS community, and stimulate production of new approaches to solving science problems. Invited talks from leading individuals from both communities will cover the state-of-the-art techniques and set the stage for this workshop.
SCOPE
We invite researchers to submit papers particularly 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
* 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
All accepted papers will be made available on the workshop website and included in the poster session during the workshop. As this does not constitute an archival publication or formal proceedings, authors are free to publish their extended work elsewhere.
Up to 6 accepted submissions will be selected for 20-minute contributed talks.
IMPORTANT DATES
* Submission deadline: November 1, 2017
* Author notification: November 10, 2017
* NIPS deadline to cancel registration: November 16, 2017
* Camera-ready deadline: December 1, 2017
* Workshop: December 8, 2017
SUBMISSIONS
Submissions should be anonymized short papers up to 4 pages in PDF format, typeset using the NIPS style (https://nips.cc/Conferences/2017/PaperInformation/StyleFiles). References do not count towards the page limit. A workshop-specific modified NIPS style file will be provided for the camera-ready versions, after the author notification date.
Submissions will be handled through the EasyChair website:
https://easychair.org/conferences/?conf=dlps2017
ORGANIZERS
Atilim Gunes Baydin (University of Oxford)
Prabhat (NERSC, Berkeley Lab)
Kyle Cranmer (New York University)
Frank Wood (University of Oxford)
REGISTRATION
Participants should refer to the NIPS 2017 website (https://nips.cc) for information on how to register for the workshop.
As of September 18, 2017, NIPS conference registrations are sold out. A limited number of registrations for workshops and tutorials are still available. We encourage participants to register as soon as possible. NIPS allows the cancellation of registrations by November 16, 2017 with full refund of the registration fee (https://nips.cc/Help/CancellationPolicy). Note that the author notification date for DLPS 2017 is November 10, 2017, to allow cancellation in the case of a submission not accepted.
CONTACT
Please direct all questions and comments to Atilim Gunes Baydin <gu...@robots.ox.ac.uk>. Please include “[DLPS 2017]” in the subject line.
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