Johann Brehmer und Kyle Cranmer - Info zur Person

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Better Higgs Measurements through Information Geometry

indico.mitp.uni-mainz.de

Webwork with Johann Brehmer, Kyle Cranmer, Tilman Plehn and Tim Tait April 19th 2018, HEFT Felix Kling Better Higgs Measurements through Information Geometry …

HEIDI: Brehmer, Johann: Better Higgs boson measurements …

katalog.ub.uni-heidelberg.de

WebJohann Brehmer, Kyle Cranmer, Felix Kling, and Tilman Plehn: E-Jahr: 2017: Jahr: 5 April 2017: Umfang: 18 S. Fussnoten: Gesehen am : Titel Quelle: Enthalten in: …

HEIDI: Brehmer, Johann: Better Higgs measurements through …

katalog.ub.uni-heidelberg.de

WebJohann Brehmer, Kyle Cranmer, Felix Kling, and Tilman Plehn: Fussnoten: Gesehen am : Titel Quelle: Enthalten in: De.arxiv.org: Jahr Quelle: 2017: Band/Heft Quelle: …

Johann Brehmer

johannbrehmer.github.io

Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer. Proceedings of the National Academy of Science 117 (2020). Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer.

Publications | Machine Learning at SLAC

ml.slac.stanford.edu

Johann Brehmer, Kyle Cranmer, Felix Kling, Int. J. of Modern Physics A 35, 15n16, (2020); PILArNet: Public Dataset for Particle Imaging Liquid Argon ...

MadMiner | Institute for Research and Innovation in Software for ...iris-hep.org › projects › madminer

iris-hep.org

Kyle Cranmer · Johann Brehmer; Irina Espejo; Sinclert Pérez; Felix Kling ... us get the most out of high-precision particle physics models", Johann Brehmer, ...

[ ] MadMiner: Machine learning-based inference for particle...

arxiv.org

Title:MadMiner: Machine learning-based inference for particle physics. Authors:Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer.

Flows for simultaneous manifold learning and density estimation

papers.nips.cc

Authors. Johann Brehmer, Kyle Cranmer. Abstract. We introduce manifold-learning flows (ℳ-flows), a new class of generative models that simultaneously learn ...

Search | arXiv e-print repository

arxiv.org

Authors: Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. Abstract: Simulators often provide the best description of real-world phenomena; however, ...

Phys. Rev. Lett. 121, (2018) - Constraining Effective Field...

journals.aps.org

Constraining Effective Field Theories with Machine Learning. Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez. Phys. Rev. Lett.

Johann Brehmer - DBLPdblp.org › Persons

dblp.org

· Johann Brehmer, Kyle Cranmer: Flows for simultaneous manifold learning and density estimation. CoRR abs (2020).

Constraining Effective Field Theories with Machine Learningjournals.aps.org › prl › PhysRevLett

journals.aps.org

Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez. Phys. Rev. Lett. 121, – Published 12 September

Johann Brehmer on SlidesLiveslideslive.at › johann-brehmer

slideslive.at

by; Johann Brehmer,; Kyle Cranmer. 82 views; Dec 6, :42. NOTAGAN: Normalizing flows for simultaneous manifold learning and density estimation ...

MadMiner — MadMiner documentation

madminer.readthedocs.io

Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer. An inference toolkit for LHC measurements. Note that this is a development version.

Publications with 10 authors or less - Kyle Cranmertheoryandpractice.org › publications

theoryandpractice.org

[ Bibtex ]; Johann Brehmer and Kyle Cranmer. Simulation-based inference methods for particle physics. To appear in Artificial Intelligence for Particle ...

The NYU Experimental Particle Physics Group - EPP @ NYU

physics.nyu.edu

Faculty Kyle Cranmer, Associate Professor Interests: ATLAS, Higgs searches, statistical methods for particle physics, improved methods for publishing, data...

Physical Review D - Volume 98 Issue 5journals.aps.org › prd › issues

journals.aps.org

Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez. Phys. Rev. D 98, (2018) – Published 12 September

Use and Citations — pyhf dev6 documentation - Scikit-HEPscikit-hep.org › pyhf › citations

scikit-hep.org

Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer. MadMiner: Machine learning-based inference for particle physics. Comput. Softw. Big Sci., 4(1):3, ...

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Gilles Louppe
Tilman Plehn
Kyle Cranmer

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Person "Brehmer" (5)
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