Sergei Gleyzer

According to our database, Sergei Gleyzer authored at least 23 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Quantum Vision Transformers for Quark-Gluon Classification.

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

Axioms, May, 2024

Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics.

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

Axioms, March, 2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks.

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

Axioms, March, 2024

ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks.

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

Axioms, March, 2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data.

Pranath Reddy, Michael W. Toomey, Hanna Parul, Sergei Gleyzer

Mach. Learn. Sci. Technol., 2024

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors.

Marco Knipfer, Stefan Meier, Tobias Volk, Jonas Heimerl, Peter Hommelhoff, Sergei Gleyzer

Mach. Learn. Sci. Technol., 2024

Quantum Diffusion Model for Quark and Gluon Jet Generation.

Mariia Baidachna, Rey Guadarrama, Gopal Ramesh Dahale, Tom Magorsch, Isabel Pedraza, Konstantin T. Matchev, Katia Matcheva, Kyoungchul Kong, Sergei Gleyzer

CoRR, 2024

Lie-Equivariant Quantum Graph Neural Networks.

Jogi Suda Neto, Roy T. Forestano, Sergei Gleyzer, Kyoungchul Kong, Konstantin T. Matchev, Katia Matcheva

CoRR, 2024

Quantum Attention for Vision Transformers in High Energy Physics.

Alessandro Tesi, Gopal Ramesh Dahale, Sergei Gleyzer, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva

CoRR, 2024

A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface.

Sofia Strukova, Sergei Gleyzer, Patrick Peplowski, Jason P. Terry

CoRR, 2024

SineKAN: Kolmogorov-Arnold Networks Using Sinusoidal Activation Functions.

Eric A. F. Reinhardt, P. R. Dinesh, Sergei Gleyzer

CoRR, 2024

2023

SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning.

Abdulhakim Alnuqaydan, Sergei Gleyzer, Harrison Prosper

Mach. Learn. Sci. Technol., March, 2023

Z₂ × Z₂ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks.

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

CoRR, 2023

Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors.

Marco Knipfer, Stefan Meier, Jonas Heimerl, Peter Hommelhoff, Sergei Gleyzer

CoRR, 2023

Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning.

J. P. Terry, C. Hall, Sean Abreau, Sergei Gleyzer

CoRR, 2023

2022

Locating Hidden Exoplanets in ALMA Data Using Machine Learning.

Jason P. Terry, Cassandra Hall, Sean Abreau, Sergei Gleyzer

CoRR, 2022

2021

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data.

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

CoRR, 2021

Graph Generative Models for Fast Detector Simulations in High Energy Physics.

Ali Hariri, Darya Dyachkova, Sergei Gleyzer

CoRR, 2021

2020

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.

Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos

Comput. Softw. Big Sci., December, 2020

2019

End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data.

Michael Andrews, John Alison, Sitong An, Patrick Bryant, Bjorn Burkle, Sergei Gleyzer, Meenakshi Narain, Manfred Paulini, Barnabás Póczos, Emanuele Usai

CoRR, 2019

2018

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.

Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabás Póczos

CoRR, 2018

Machine Learning in High Energy Physics Community White Paper.

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

CoRR, 2018

2017

Accelerating High-energy Physics Exploration with Deep Learning.

Dave Ojika, Darin Acosta, Ann Gordon-Ross, Andrew Carnes, Sergei Gleyzer

Proceedings of the Practice and Experience in Advanced Research Computing 2017: Sustainability, 2017