Listing of useful (mostly) public learning resources for machine learning applications in high energy physics (HEPML). Listings will be in reverse chronological order (like a CV).
N.B.: This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most. However, this is not the desired case and help to diversify this listing would be greatly appreciated.
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CERN Academic Training Lecture Regular Programme, April 2017 (Machine Learning):
- Machine Learning (Lecture 1) --- Michael Kagan (SLAC)
- Machine Learning (Lecture 2) --- Michael Kagan (SLAC)
- Deep Learning and Vision --- Jonathon Shlens (Google Research)
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Introduction to Deep Learning with Keras Tutorial, Luke de Oliveira
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Applications of Deep Learning to High Energy Physics, Amir Farbin (Spring, 2017 - University of Texas at Arlington)
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Tensorflow for Deep Learning Research, (Spring, 2017 - Stanford Univeristy)
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Introduction to Machine Learning and Convolutional Neural Networks for Visual Recognition, Andrej Karpathy (Winter, 2016 - Stanford Univeristy)
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Machine Learning in High Energy Physics 2016, Yandex School of Data Analysis (Summer, 2016 - Lund University)
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The Conda package and environment manager and Anaconda Python library collection
- QCD-Aware Neural Networks for Jet Physics, Kyle Cranmer
- (Machine) Learning to Pivot with Adversarial Networks, Gilles Louppe
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M. Paganini, L. de Oliveira, and B. Nachman, "CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks," arXiv:1705.02355 [hep-ex]. (May 5, 2017)
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C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Sgaard, "Decorrelated Jet Substructure Tagging using Adversarial Neural Networks," arXiv:1703.03507 [hep-ex]. (March 9, 2017)
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G. Louppe, K. Cho, C. Becot, and K. Cranmer, "QCD-Aware Recursive Neural Networks for Jet Physics," arXiv:1702.00748 [hep-ph]. (February 2, 2017)
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L. de Oliveira, M. Paganini, and B. Nachman, "Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis," arXiv:1701.05927 [stat.ML]. (January 20, 2017)
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P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, "Deep learning in color: towards automated quark/gluon jet discrimination," JHEP 01 (2017) 110, arXiv:1612.01551 [hep-ph]. (December 5, 2016)
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MicroBooNE Collaboration, R. Acciarri et al., "Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber," JINST 12 (2017) no. 03, P03011, arXiv:1611.05531 [physics.ins-det]. (November 16, 2016)
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M. Kagan, L. d. Oliveira, L. Mackey, B. Nachman, and A. Schwartzman, "Boosted Jet Tagging with Jet-Images and Deep Neural Networks," EPJ Web Conf. 127 (2016) 00009. (November 15, 2016)
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G. Bertone, M. P. Deisenroth, J. S. Kim, S. Liem, R. Ruiz de Austri, and M. Welling, "Accelerating the BSM interpretation of LHC data with machine learning," arXiv:1611.02704 [hep-ph]. (November 8, 2016)
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G. Louppe, M. Kagan, and K. Cranmer, "Learning to Pivot with Adversarial Networks," arXiv:1611.01046 [stat.ME]. (November 3, 2016)
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J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, "Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks," Phys. Rev. D95 (2017) no. 1, 014018, arXiv:1609.00607 [hep-ph] (September 2, 2016)
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S. Caron, J.S. Kim, K. Rolbiecki, R. Ruiz de Austri, B. Stienen "The BSM-AI project: SUSY-AI -- Generalizing LHC limits on supersymmetry with machine learning", EPJ C (2017) 77:257, arXiv:1605.02797 [hep-ph]. (May 9, 2016)
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A. Aurisano, A. Radovic, D. Rocco, A. Himmel, M. D. Messier, E. Niner, G. Pawloski, F. Psihas, A. Sousa, and P. Vahle, "A Convolutional Neural Network Neutrino Event Classifier," JINST 11 (2016) no. 09, P09001, arXiv:1604.01444 [hep-ex]. (April 5, 2016)
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L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, "Jet-images deep learning edition," JHEP 07 (2016) 069, arXiv:1511.05190 [hep-ph]. (November 16, 2015)
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P. Baldi, P. Sadowski, and D. Whiteson, "Searching for Exotic Particles in High-Energy Physics with Deep Learning," Nature Commun. 5 (2014) 4308, arXiv:1402.4735 [hep-ph]. (February 19, 2014)
- Hammers & Nails - Machine Learning & HEP (July 19-28, 2017)
- Third Machine Learning in High Energy Physics Summer School 2017 (July 17-23, 2017)
- First Computational and Data Science School for High Energy Physics (July 10-13, 2017)
- ATLAS Machine Learning Workshop (2017) (June 6-9, 2017)
- Workshop on Machine Learning and b-tagging (May 23-26, 2017)
- DS@HEP 2017 (May 8-12, 2017)
- 2nd S2I2 HEP/CS Workshop (Parallel Session) (May 1-3, 2017)
- CERN openlab workshop on Machine Learning and Data Analytics (April 27, 2017)
- First IML Workshop on Machine Learning (March 20-22, 2017)
- DS@HEP at the Simons Foundation (July 5-7, 2016)
- Second Machine Learning in High Energy Physics Summer School 2016 (June 20-26, 2016)
- ALICE Mini-Workshop 2016: Statistical Methods and Machine Learning Tutorial (May 18, 2016)
- ATLAS Machine Learning Workshop (2016) (March 29-31, 2016)
- Data Science @ LHC 2015 (November 9-13, 2015)
Contributions to help improve the listing are very much welcome! Please read CONTRIBUTING.md for details on the process for submitting pull requests or filing issues.
Listing maintainer: Matthew Feickert
- Following PurpleBooth's README style
- All badges made by shields.io
- Inspiration for this listing came from the Awesome Machine Learning repo