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Journal Articles
- 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)- 2020
- [j4]Craig B. Stunkel, Richard L. Graham, Gilad Shainer, Michael Kagan, S. S. Sharkawi, Bryan S. Rosenburg, George Chochia:
The high-speed networks of the Summit and Sierra supercomputers. IBM J. Res. Dev. 64(3/4): 3:1-3:10 (2020) - 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) - 2013
- [j2]Gilad Shainer, Todd Wilde, Pak Lui, Tong Liu, Michael Kagan, Mike Dubman, Yiftah Shahar, Richard L. Graham, Pavel Shamis, Steve Poole:
The co-design architecture for exascale systems, a novel approach for scalable designs. Comput. Sci. Res. Dev. 28(2-3): 119-125 (2013) - 2011
- [j1]Gilad Shainer, Ali Ayoub, Pak Lui, Tong Liu, Michael Kagan, Christian Trott, Greg Scantlen, Paul S. Crozier:
The development of Mellanox/NVIDIA GPUDirect over InfiniBand - a new model for GPU to GPU communications. Comput. Sci. Res. Dev. 26(3-4): 267-273 (2011)
Conference and Workshop Papers
- 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 - 2020
- [c9]Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atilim Gunes Baydin:
Black-Box Optimization with Local Generative Surrogates. NeurIPS 2020 - 2017
- [c8]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. NIPS 2017: 981-990 - 2010
- [c7]Richard L. Graham, Steve Poole, Pavel Shamis, Gil Bloch, Noam Bloch, Hillel Chapman, Michael Kagan, Ariel Shahar, Ishai Rabinovitz, Gilad Shainer:
ConnectX-2 InfiniBand Management Queues: First Investigation of the New Support for Network Offloaded Collective Operations. CCGRID 2010: 53-62 - [c6]Richard L. Graham, Stephen W. Poole, Pavel Shamis, Gil Bloch, Noam Bloch, Hillel Chapman, Michael Kagan, Ariel Shahar, Ishai Rabinovitz, Gilad Shainer:
Overlapping computation and communication: Barrier algorithms and ConnectX-2 CORE-Direct capabilities. IPDPS Workshops 2010: 1-8 - 2009
- [c5]Gilad Shainer, Eyal Gutkind, Bill Lee, Michael Kagan, Yevgeny Kliteynik:
Optics for Enabling Future HPC Systems. Hot Interconnects 2009: 138-142 - 2006
- [c4]Michael Kagan:
InfiniBand interconnects - Application acceleration through MPI overlap. SC 2006: 268 - 2005
- [c3]Dror Goldenberg, Michael Kagan, Ran Ravid, Michael S. Tsirkin:
Transparently Achieving Superior Socket Performance Using Zero Copy Socket Direct Protocol over 20Gb/s InfiniBand Links. CLUSTER 2005: 1-10 - [c2]Dror Goldenberg, Michael Kagan, Ran Ravid, Michael S. Tsirkin:
Zero Copy Sockets Direct Protocol over InfiniBand - Preliminary Implementation and Performance Analysis. Hot Interconnects 2005: 128-137 - 1997
- [c1]Mooly Eden, Michael Kagan:
The Pentium® processor with MMX™ technology. COMPCON 1997: 260-262
Informal and Other Publications
- 2024
- [i18]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) - [i17]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) - [i16]Matthew Leigh, Samuel Klein, François Charton, Tobias Golling, Lukas Heinrich, Michael Kagan, Inês Ochoa, Margarita Osadchy:
Is Tokenization Needed for Masked Particle Modelling? CoRR abs/2409.12589 (2024) - 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
- [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
- [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) - [i4]Michael Kagan:
Image-Based Jet Analysis. CoRR abs/2012.09719 (2020) - 2019
- [i3]Siavash Golkar, Michael Kagan, Kyunghyun Cho:
Continual Learning via Neural Pruning. CoRR abs/1903.04476 (2019) - 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) - 2016
- [i1]Gilles Louppe, Michael Kagan, Kyle Cranmer:
Learning to Pivot with Adversarial Networks. CoRR abs/1611.01046 (2016)
Coauthor Index
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