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Michael Kagan
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Publications
- 2024
- [i17]Lukas Heinrich, Tobias Golling, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine:
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models. CoRR abs/2401.13537 (2024) - [i16]Philip C. Harris, Michael Kagan, Jeffrey D. Krupa, Benedikt Maier, Nathaniel Woodward:
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models. CoRR abs/2403.07066 (2024) - 2023
- [j5]Elham E Khoda, Dylan S. Rankin, Rafael Teixeira de Lima, Philip C. Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang:
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. Mach. Learn. Sci. Technol. 4(2): 25004 (2023) - [i15]Michael Kagan, Lukas Heinrich:
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics. CoRR abs/2308.16680 (2023) - [i14]Rachel E. C. Smith, Inês Ochoa, Rúben Inácio, Jonathan Shoemaker, Michael Kagan:
Differentiable Vertex Fitting for Jet Flavour Tagging. CoRR abs/2310.12804 (2023) - 2022
- [i13]Lukas Heinrich, Michael Kagan:
Differentiable Matrix Elements with MadJax. CoRR abs/2203.00057 (2022) - [i12]Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner:
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation. CoRR abs/2203.08806 (2022) - [i11]Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier M. Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao:
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges. CoRR abs/2203.12852 (2022) - [i10]Sanha Cheong, Josef C. Frisch, Sean Gasiorowski, Jason M. Hogan, Michael Kagan, Murtaza Safdari, Ariel Schwartzman, Maxime Vandegar:
Novel Light Field Imaging Device with Enhanced Light Collection for Cold Atom Clouds. CoRR abs/2205.11480 (2022) - [i9]Elham E Khoda, Dylan S. Rankin, Rafael Teixeira de Lima, Philip C. Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar, Chaitanya Paikara, Richa Rao, Sioni Summers, Caterina Vernieri, Aaron Wang:
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml. CoRR abs/2207.00559 (2022) - [i8]Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra:
Interpretable Uncertainty Quantification in AI for HEP. CoRR abs/2208.03284 (2022) - 2021
- [c11]Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe:
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. AISTATS 2021: 2107-2115 - [c10]Youssef S. G. Nashed, Frédéric Poitevin, Harshit Gupta, Geoffrey Woollard, Michael Kagan, Chun Hong Yoon, Daniel Ratner:
CryoPoseNet: End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data. ICCVW 2021: 4049-4059 - [i7]Youssef S. G. Nashed, Frédéric Poitevin, Harshit Gupta, Geoffrey Woollard, Michael Kagan, Chuck Yoon, Daniel Ratner:
End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data. CoRR abs/2107.02958 (2021) - 2020
- [c9]Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atilim Gunes Baydin:
Black-Box Optimization with Local Generative Surrogates. NeurIPS 2020 - [i6]Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atilim Günes Baydin:
Differentiating the Black-Box: Optimization with Local Generative Surrogates. CoRR abs/2002.04632 (2020) - [i5]Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe:
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference. CoRR abs/2011.05836 (2020) - 2019
- [i3]Siavash Golkar, Michael Kagan, Kyunghyun Cho:
Continual Learning via Neural Pruning. CoRR abs/1903.04476 (2019) - 2018
- [j3]Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, Taritree Wongjirad:
Machine learning at the energy and intensity frontiers of particle physics. Nat. 560(7716): 41-48 (2018) - [i2]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 M. 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 V. 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, Przemyslaw Karpinski, 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 S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. 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 Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham 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:
Machine Learning in High Energy Physics Community White Paper. CoRR abs/1807.02876 (2018) - 2017
- [c8]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. NIPS 2017: 981-990 - 2016
- [i1]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. CoRR abs/1611.01046 (2016)
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