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A neural simulation-based inference approach for characterizing the Galactic Center $╬│$-ray excess


Oct 13, 2021
Siddharth Mishra-Sharma, Kyle Cranmer

* 20+3 pages, 10+4 figures 

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Flow-based sampling for multimodal distributions in lattice field theory


Jul 01, 2021
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan

* 33 pages, 29 figures 

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Flow-based sampling for fermionic lattice field theories


Jun 10, 2021
Michael S. Albergo, Gurtej Kanwar, S├ębastien Racani├Ęre, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan

* 26 pages, 5 figures 

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Exact and Approximate Hierarchical Clustering Using A*


Apr 14, 2021
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum

* 30 pages, 9 figures 

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Introduction to Normalizing Flows for Lattice Field Theory


Jan 20, 2021
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, S├ębastien Racani├Ęre, Danilo Jimenez Rezende, Phiala E. Shanahan

* 38 pages, 5 numbered figures, Jupyter notebook included as ancillary file 

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Hierarchical clustering in particle physics through reinforcement learning


Nov 16, 2020
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, Kyle Cranmer

* Accepted at the Machine Learning and the Physical Sciences workshop at NeurIPS 2020 

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Simulation-based inference methods for particle physics


Nov 02, 2020
Johann Brehmer, Kyle Cranmer

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

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Semi-parametric $╬│$-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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Sampling using $SU(N)$ gauge equivariant flows


Aug 12, 2020
Denis Boyda, Gurtej Kanwar, S├ębastien Racani├Ęre, 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, S├ębastien Racani├Ęre, 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, S├ębastien Racani├Ęre, 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
At─▒l─▒m G├╝ne┼č 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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