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Track Seeding and Labelling with Embedded-space Graph Neural Networks

Jun 30, 2020
Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

* Proceedings submission in Connecting the Dots Workshop 2020, 10 pages 

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MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

May 01, 2020
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

* Supplementary Video: https://youtu.be/mjqwPch9gDo 

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Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Oct 29, 2019
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David Barajas-Solano, Josh Romero, Valentin Churavy, Alexandre Tartakovsky, Michael Houston, Prabhat, George Karniadakis

* 3rd Deep Learning on Supercomputers Workshop (DLS) at SC19 

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DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems

Sep 25, 2019
Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor Lee, Prabhat, James P. Crutchfield


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Towards Unsupervised Segmentation of Extreme Weather Events

Sep 16, 2019
Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor Lee, Prabhat, James P. Crutchfield


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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Jul 08, 2019
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip Torr, Victor Lee, Kyle Cranmer, Prabhat, Frank Wood

* 14 pages, 8 figures 

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Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

May 13, 2019
Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao


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Spherical CNNs on Unstructured Grids

Jan 07, 2019
Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

* Accepted as a conference paper at ICLR 2019. Codes available at https://github.com/maxjiang93/ugscnn 

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Approximate Inference for Constructing Astronomical Catalogs from Images

Oct 12, 2018
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

* major revision for AoAS 

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Graph Neural Networks for IceCube Signal Classification

Sep 17, 2018
Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna


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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

Sep 01, 2018
Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank Wood

* 18 pages, 5 figures 

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Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale

Aug 21, 2018
Mahesh Balasubramanian, Trevor Ruiz, Brandon Cook, Sharmodeep Bhattacharyya, Prabhat, Aviral Shrivastava, Kristofer Bouchard

* 10 pages, 10 figures 

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CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Aug 14, 2018
Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

* 12 pages, 6 pages, accepted to SuperComputing 2018 

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

Dec 21, 2017
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

* 7 pages, 2 figures 

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Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

Nov 29, 2017
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah

* Presented at ACAT 2017 Conference, Submitted to J. Phys. Conf. Ser 

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ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events

Nov 25, 2017
Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal


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Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

Nov 02, 2017
Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine M. Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya

* 42 pages; a conference version is in NIPS 2017 

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

Aug 17, 2017
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey

* 12 pages, 9 figures 

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Dec 06, 2016
Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat


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Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference

Nov 10, 2016
Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat

* submitting to IPDPS'17 

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Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

May 04, 2016
Yunjie Liu, Evan Racah, Prabhat, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, William Collins


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Scalable Bayesian Optimization Using Deep Neural Networks

Jul 13, 2015
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams


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Celeste: Variational inference for a generative model of astronomical images

Jun 03, 2015
Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat

* in the Proceedings of the 32nd International Conference on Machine Learning (2015) 

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