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Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence



Ashesh Chattopadhyay , Jaideep Pathak , Ebrahim Nabizadeh , Wahid Bhimji , Pedram Hassanzadeh


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The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments



Venkitesh Ayyar , Wahid Bhimji , Lisa Gerhardt , Sally Robertson , Zahra Ronaghi

* Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia 

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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale



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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Graph Neural Networks for IceCube Signal Classification



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



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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Creating Virtual Universes Using Generative Adversarial Networks



Mustafa Mustafa , Deborah Bard , Wahid Bhimji , Zarija Lukić , Rami Al-Rfou , Jan Kratochvil

* 9 pages, 8 figures 

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Machine Learning in High Energy Physics Community White Paper



Kim Albertsson , Piero Altoe , Dustin Anderson , Michael Andrews , Juan Pedro Araque Espinosa , Adam Aurisano , Laurent Basara , Adrian Bevan , Wahid Bhimji , Daniele Bonacorsi , Paolo Calafiura , Mario Campanelli , Louis Capps , Federico Carminati , Stefano Carrazza , Taylor Childers , Elias Coniavitis , Kyle Cranmer , Claire David , Douglas Davis , Javier Duarte , Martin Erdmann , Jonas Eschle , Amir Farbin , Matthew Feickert , Nuno Filipe Castro , Conor Fitzpatrick , Michele Floris , Alessandra Forti , Jordi Garra-Tico , Jochen Gemmler , Maria Girone , Paul Glaysher , Sergei Gleyzer , Vladimir Gligorov , Tobias Golling , Jonas Graw , Lindsey Gray , Dick Greenwood , Thomas Hacker , John Harvey , Benedikt Hegner , Lukas Heinrich , Ben Hooberman , Johannes Junggeburth , Michael Kagan , Meghan Kane , Konstantin Kanishchev , Przemysław Karpiński , Zahari Kassabov , Gaurav Kaul , Dorian Kcira , Thomas Keck , Alexei Klimentov , Jim Kowalkowski , Luke Kreczko , Alexander Kurepin , Rob Kutschke , Valentin Kuznetsov , Nicolas Köhler , Igor Lakomov , Kevin Lannon , Mario Lassnig , Antonio Limosani , Gilles Louppe , Aashrita Mangu , Pere Mato , Narain Meenakshi , Helge Meinhard , Dario Menasce , Lorenzo Moneta , Seth Moortgat , Mark Neubauer , Harvey Newman , Hans Pabst , Michela Paganini , Manfred Paulini , Gabriel Perdue , Uzziel Perez , Attilio Picazio , Jim Pivarski , Harrison Prosper , Fernanda Psihas , Alexander Radovic , Ryan Reece , Aurelius Rinkevicius , Eduardo Rodrigues , Jamal Rorie , David Rousseau , Aaron Sauers , Steven Schramm , Ariel Schwartzman , Horst Severini , Paul Seyfert , Filip Siroky , Konstantin Skazytkin , Mike Sokoloff , Graeme Stewart , Bob Stienen , Ian Stockdale , Giles Strong , Savannah Thais , Karen Tomko , Eli Upfal , Emanuele Usai , Andrey Ustyuzhanin , Martin Vala , Sofia Vallecorsa , Mauro Verzetti , Xavier Vilasís-Cardona , Jean-Roch Vlimant , Ilija Vukotic , Sean-Jiun Wang , Gordon Watts , Michael Williams , Wenjing Wu , Stefan Wunsch , Omar Zapata

* Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm 

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



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



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



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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