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2020 – today
- 2024
[j8]Roy T. Forestano
, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks. Axioms 13(3): 160 (2024)- [j7]Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva:
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics. Axioms 13(3): 187 (2024) - [j6]Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. Axioms 13(3): 188 (2024) - [j5]Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
Quantum Vision Transformers for Quark-Gluon Classification. Axioms 13(5): 323 (2024) - [j4]Marco Knipfer, Stefan Meier, Tobias Volk, Jonas Heimerl, Peter Hommelhoff, Sergei Gleyzer:
Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors. Mach. Learn. Sci. Technol. 5(2): 25019 (2024) - [j3]Pranath Reddy, Michael W. Toomey, Hanna Parul, Sergei Gleyzer:
DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data. Mach. Learn. Sci. Technol. 5(3): 35076 (2024) - [i15]Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva:
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics. CoRR abs/2402.00776 (2024) - [i14]Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
Quantum Vision Transformers for Quark-Gluon Classification. CoRR abs/2405.10284 (2024) - [i13]Eric A. F. Reinhardt, P. R. Dinesh, Sergei Gleyzer:
SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions. CoRR abs/2407.04149 (2024) - [i12]Sofia Strukova, Sergei Gleyzer, Patrick Peplowski, Jason P. Terry:
A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface. CoRR abs/2407.05832 (2024) - 2023
- [j2]Abdulhakim Alnuqaydan, Sergei Gleyzer, Harrison Prosper:
SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning. Mach. Learn. Sci. Technol. 4(1): 15007 (2023) - [i11]J. P. Terry, C. Hall, Sean Abreau, Sergei Gleyzer:
Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning. CoRR abs/2301.05075 (2023) - [i10]Marco Knipfer, Stefan Meier, Jonas Heimerl, Peter Hommelhoff, Sergei Gleyzer:
Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors. CoRR abs/2306.09359 (2023) - [i9]Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks. CoRR abs/2311.18672 (2023) - [i8]Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu:
Z2 × Z2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks. CoRR abs/2311.18744 (2023) - 2022
- [i7]Abdulhakim Alnuqaydan, Sergei Gleyzer, Harrison Prosper:
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning. CoRR abs/2206.08901 (2022) - [i6]Jason P. Terry, Cassandra Hall, Sean Abreau, Sergei Gleyzer:
Locating Hidden Exoplanets in ALMA Data Using Machine Learning. CoRR abs/2211.09541 (2022) - 2021
- [i5]Ali Hariri, Darya Dyachkova, Sergei Gleyzer:
Graph Generative Models for Fast Detector Simulations in High Energy Physics. CoRR abs/2104.01725 (2021) - [i4]Michael Andrews, Bjorn Burkle, Yi-Fan Chen, Davide DiCroce, Sergei Gleyzer, Ulrich Heintz, Meenakshi Narain, Manfred Paulini, Nikolas Pervan, Yusef Shafi, Wei Sun, Kun Yang:
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data. CoRR abs/2104.14659 (2021) - 2020
- [j1]Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos:
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. Comput. Softw. Big Sci. 4(1) (2020)
2010 – 2019
- 2019
- [i3]Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabás Póczos, Emanuele Usai:
End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. CoRR abs/1902.08276 (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) - [i1]Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos:
End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC. CoRR abs/1807.11916 (2018) - 2017
- [c1]Dave Ojika, Darin Acosta, Ann Gordon-Ross, Andrew Carnes, Sergei Gleyzer:
Accelerating High-energy Physics Exploration with Deep Learning. PEARC 2017: 37:1-37:4
Coauthor Index
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