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Workshop
Fri Dec 08 08:00 AM -- 06:30 PM (PST) @ 104 C
Deep Learning for Physical Sciences
Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood





Workshop Home Page

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.

Introduction and opening remarks (Talk)
Invited talk 1: Deep recurrent inverse modeling for radio astronomy and fast MRI imaging (Talk)
Contributed talk 1: Neural Message Passing for Jet Physics (Talk)
Contributed talk 2: A Foray into Using Neural Network Control Policies For Rapid Switching Between Beam Parameters in a Free Electron Laser (Talk)
Poster session 1 and coffee break (Poster Session)
Invited talk 2: Adversarial Games for Particle Physics (Talk)
Contributed talk 3: Implicit Causal Models for Genome-wide Association Studies (Talk)
Contributed talk 4: Graphite: Iterative Generative Modeling of Graphs (Talk)
Sponsor presentation: Intel Nervana (Talk)
Lunch break (Break)
Invited talk 3: Learning priors, likelihoods, or posteriors (Talk)
Contributed talk 5: Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Real LIGO Data (Talk)
Poster session 2 and coffee break (Poster Session)
Invited talk 4: A machine learning perspective on the many-body problem in classical and quantum physics (Talk)
Invited talk 5: Quantum Machine Learning (Talk)
Contributed talk 6: Physics-guided Learning of Neural Networks: An Application in Lake Temperature Modeling (Talk)
Panel session (Panel Discussion)
Closing remarks (Talk)