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Semi-parametric $γ$-ray modeling with Gaussian processes and variational inference

Oct 20, 2020
Siddharth Mishra-Sharma, Kyle Cranmer

* 8 pages, 1 figure, extended abstract submitted to the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020 

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Simulation-based inference methods for particle physics

Oct 13, 2020
Johann Brehmer, Kyle Cranmer

* To appear in "Artificial Intelligence for Particle Physics", World Scientific Publishing Co 

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Sampling using $SU(N)$ gauge equivariant flows

Aug 12, 2020
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

* 22 pages, 19 figures 

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Discovering Symbolic Models from Deep Learning with Inductive Biases

Jun 19, 2020
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho

* 9 pages content + 14 pages appendix/references. Supporting code found at https://github.com/MilesCranmer/symbolic_deep_learning 

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Flows for simultaneous manifold learning and density estimation

Mar 31, 2020
Johann Brehmer, Kyle Cranmer

* Code at https://github.com/johannbrehmer/manifold-flow 

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Equivariant flow-based sampling for lattice gauge theory

Mar 13, 2020
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan

* 6 pages, 4 figures 

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Compact Representation of Uncertainty in Hierarchical Clustering

Feb 26, 2020
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji-Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum

* 21 pages, 5 figures 

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Set2Graph: Learning Graphs From Sets

Feb 20, 2020
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman


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Normalizing Flows on Tori and Spheres

Feb 06, 2020
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer


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The frontier of simulation-based inference

Nov 14, 2019
Kyle Cranmer, Johann Brehmer, Gilles Louppe

* v2 fixed typos. 8 pages, 3 figures, proceedings for the Sackler Colloquia at the US National Academy of Sciences 

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Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Oct 17, 2019
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

* 23 pages, 6 figures, code available at https://github.com/smsharma/mining-for-substructure-lens; v2, minor changes to text, version accepted in ApJ 

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Hamiltonian Graph Networks with ODE Integrators

Sep 27, 2019
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter Battaglia


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MadMiner: Machine learning-based inference for particle physics

Jul 24, 2019
Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

* MadMiner is available at https://github.com/diana-hep/madminer 

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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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Effective LHC measurements with matrix elements and machine learning

Jun 04, 2019
Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, Gilles Louppe, Juan Pavez

* Keynote at the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019) 

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Inferring the quantum density matrix with machine learning

Apr 11, 2019
Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo

* 12 pages, 3 figures 

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Mining gold from implicit models to improve likelihood-free inference

Oct 09, 2018
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example 

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Adversarial Variational Optimization of Non-Differentiable Simulators

Oct 05, 2018
Gilles Louppe, Joeri Hermans, Kyle Cranmer


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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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Likelihood-free inference with an improved cross-entropy estimator

Aug 02, 2018
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* 8 pages, 3 figures 

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A Guide to Constraining Effective Field Theories with Machine Learning

Jul 26, 2018
Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. D 98, 052004 (2018) 
* See also the companion publication "Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00013, a brief introduction presenting the key ideas. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: Added references. v3: Improved description of algorithms, added references. v4: Clarified text, added references 

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Constraining Effective Field Theories with Machine Learning

Jul 26, 2018
Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. Lett. 121, 111801 (2018) 
* See also the companion publication "A Guide to Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00020, an in-depth analysis of machine learning techniques for LHC measurements. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: New schematic figure explaining the new algorithms, added references. v3, v4: Added references 

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QCD-Aware Recursive Neural Networks for Jet Physics

Jul 13, 2018
Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer

* 16 pages, 5 figures, 3 appendices, corresponding code at https://github.com/glouppe/recnn 

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Machine Learning in High Energy Physics Community White Paper

Jul 08, 2018
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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Backdrop: Stochastic Backpropagation

Jun 04, 2018
Siavash Golkar, Kyle Cranmer

* 11 pages, 9 figures, 2 tables. Source code available at https://github.com/dexgen/backdrop 

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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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Learning to Pivot with Adversarial Networks

Jun 01, 2017
Gilles Louppe, Michael Kagan, Kyle Cranmer

* v1: Original submission. v2: Fixed references. v3: version submitted to NIPS'2017. Code available at https://github.com/glouppe/paper-learning-to-pivot 

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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Mar 18, 2016
Kyle Cranmer, Juan Pavez, Gilles Louppe

* 35 pages, 5 figures 

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