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ORCIDTobias Golling
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2020 – today
- 2025
[i31]Kristian Gonzalez Barman, Sascha Caron, Emily Sullivan, Henk W. de Regt, Roberto Ruiz de Austri, Mieke Boon, Michael Färber, Stefan Fröse
, Faegheh Hasibi, Andreas Ipp, Rukshak Kapoor, Gregor Kasieczka, Daniel Kostic, Michael Krämer, Tobias Golling, Luis G. Lopez, Jesus Marco, Sydney Otten, Pawel Pawlowski, Pietro Vischia, Erik Weber, Christoph Weniger:
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models. CoRR abs/2501.05382 (2025)- [i30]Ivan Oleksiyuk, Svyatoslav Voloshynovskiy, Tobias Golling:
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches. CoRR abs/2503.04342 (2025) - [i29]Sascha Caron, Andreas Ipp, Gert Aarts, Gábor Bíró, Daniele Bonacorsi, Elena Cuoco, Caterina Doglioni, Tommaso Dorigo, Julián García Pardiñas, Stefano Giagu, Tobias Golling, Lukas Heinrich, Ik Siong Heng, Paula Gina Isar, Karolos Potamianos, Liliana Teodorescu, John Veitch, Pietro Vischia, Christoph Weniger:
Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF. CoRR abs/2503.14192 (2025) - [i28]Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler, Markus Klute, François Fleuret, Tobias Golling:
End-to-End Optimal Detector Design with Mutual Information Surrogates. CoRR abs/2503.14342 (2025) - 2024
- [j3]John Andrew Raine, Samuel Klein, Debajyoti Sengupta, Tobias Golling:
CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals. Frontiers Big Data 6 (2024) - [j2]Tobias Golling, Lukas Heinrich, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine:
Masked particle modeling on sets: towards self-supervised high energy physics foundation models. Mach. Learn. Sci. Technol. 5(3): 35074 (2024) - [i27]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) - [i26]Guillaume Quétant, John Andrew Raine, Matthew Leigh, Debajyoti Sengupta, Tobias Golling:
PIPPIN: Generating variable length full events from partons. CoRR abs/2406.13074 (2024) - [i25]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) - [i24]Malte Algren, Chris Pollard, John Andrew Raine, Tobias Golling:
Variational inference for pile-up removal at hadron colliders with diffusion models. CoRR abs/2410.22074 (2024) - [i23]Franck Rothen, Samuel Klein, Matthew Leigh, Tobias Golling:
Enhancing generalization in high energy physics using white-box adversarial attacks. CoRR abs/2411.09296 (2024) - 2023
- [j1]Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Dmitry Emeliyanov, Victor Estrade, Steven Farrell, Cécile Germain, Vladimir Vava Gligorov, Tobias Golling, Sergey Gorbunov, Heather M. Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Moritz Kiehn, Marcel Kunze, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, Jean-Roch Vlimant:
The Tracking Machine Learning Challenge: Throughput Phase. Comput. Softw. Big Sci. 7(1): 1 (2023) - [c5]Atul Kumar Sinha, Daniele Paliotta, Bálint Máté, John A. Raine, Tobias Golling, François Fleuret:
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. NeurIPS 2023 - [i22]Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant, John Andrew Raine, Knut Zoch, Tobias Golling:
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics. CoRR abs/2303.05376 (2023) - [i21]Lukas Ehrke, John Andrew Raine, Knut Zoch, Manuel Guth, Tobias Golling:
Topological Reconstruction of Particle Physics Processes using Graph Neural Networks. CoRR abs/2303.13937 (2023) - [i20]Malte Algren, Tobias Golling, Manuel Guth, Chris Pollard, John Andrew Raine:
Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting. CoRR abs/2304.14963 (2023) - [i19]Debajyoti Sengupta, Samuel Klein, John Andrew Raine, Tobias Golling:
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation. CoRR abs/2305.04646 (2023) - [i18]John Andrew Raine, Matthew Leigh, Knut Zoch, Tobias Golling:
ν2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows. CoRR abs/2307.02405 (2023) - [i17]Malte Algren, John Andrew Raine, Tobias Golling:
Decorrelation using Optimal Transport. CoRR abs/2307.05187 (2023) - [i16]Matthew Leigh, Debajyoti Sengupta, John Andrew Raine, Guillaume Quétant, Tobias Golling:
PC-Droid: Faster diffusion and improved quality for particle cloud generation. CoRR abs/2307.06836 (2023) - [i15]Tobias Golling, Samuel Klein, Radha Mastandrea, Benjamin Nachman, John Andrew Raine:
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation. CoRR abs/2309.06472 (2023) - [i14]Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih:
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion. CoRR abs/2310.00049 (2023) - [i13]Debajyoti Sengupta, Matthew Leigh, John Andrew Raine, Samuel Klein, Tobias Golling:
Improving new physics searches with diffusion models for event observables and jet constituents. CoRR abs/2312.10130 (2023) - 2022
- [c4]Bálint Máté, Samuel Klein, Tobias Golling, François Fleuret:
Flowification: Everything is a normalizing flow. NeurIPS 2022 - [i12]Atul Kumar Sinha, Daniele Paliotta, Bálint Máté, Sebastian Pina-Otey, John A. Raine, Tobias Golling, François Fleuret:
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics. CoRR abs/2202.05012 (2022) - [i11]Bálint Máté, Samuel Klein, Tobias Golling, François Fleuret:
Flowification: Everything is a Normalizing Flow. CoRR abs/2205.15209 (2022) - [i10]Matthew Leigh, John Andrew Raine, Knut Zoch, Tobias Golling:
ν-Flows: Conditional Neutrino Regression. CoRR abs/2207.00664 (2022) - [i9]Samuel Klein, Tobias Golling:
Decorrelation with conditional normalizing flows. CoRR abs/2211.02486 (2022) - [i8]Samuel Klein, John Andrew Raine, Tobias Golling:
Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation. CoRR abs/2211.02487 (2022) - 2021
- [i7]Sabrina Amrouche, Moritz Kiehn, Tobias Golling, Andreas Salzburger:
Hashing and metric learning for charged particle tracking. CoRR abs/2101.06428 (2021) - [i6]Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Dmitry Emeliyanov, Victor Estrade, Steven Farrell, Cécile Germain, Vladimir Vava Gligorov, Tobias Golling, Sergey Gorbunov, Heather M. Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Moritz Kiehn, Marcel Kunze, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, Jean-Roch Vlimant:
The Tracking Machine Learning challenge : Throughput phase. CoRR abs/2105.01160 (2021) - [i5]Samuel Klein, John A. Raine, Sebastian Pina-Otey, Slava Voloshynovskiy, Tobias Golling:
Funnels: Exact maximum likelihood with dimensionality reduction. CoRR abs/2112.08069 (2021) - [i4]Mariia Drozdova, Vitaliy Kinakh, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy:
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks. CoRR abs/2112.09646 (2021) - [i3]Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy:
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN. CoRR abs/2112.09653 (2021) - [i2]Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh, Tobias Golling, Slava Voloshynovskiy:
Turbo-Sim: a generalised generative model with a physical latent space. CoRR abs/2112.10629 (2021)
2010 – 2019
- 2019
- [c3]Sabrina Amrouche, Tobias Golling, Moritz Kiehn, Claudia Plant, Andreas Salzburger:
Similarity hashing for charged particle tracking. IEEE BigData 2019: 1595-1600 - 2018
- [c2]Paolo Calafiura, Steven Farrell, Heather M. Gray, Jean-Roch Vlimant, Vincenzo Innocente, Andreas Salzburger, Sabrina Amrouche, Tobias Golling, Moritz Kiehn, Victor Estrade, Cécile Germain, Isabelle Guyon, Ed Moyse, David Rousseau, Yetkin Yilmaz, Vladimir Vava Gligorov, Mikhail Hushchyn, Andrey Ustyuzhanin:
TrackML: A High Energy Physics Particle Tracking Challenge. eScience 2018: 344 - [c1]Dalila Salamani, Stefan Gadatsch, Tobias Golling, Graeme Andrew Stewart, Aishik Ghosh, David Rousseau, Ahmed Hasib, Jana Schaarschmidt:
Deep Generative Models for Fast Shower Simulation in ATLAS. eScience 2018: 348 - [d2]Andreas Salzburger, Vincenzo Innocente, Jean-Roch Vlimant, David Rousseau, Vladimir V. Gligorov, Laurent Basara, Victor Estrade, Paolo Calafiura, Steven Farell, Heather M. Gray, Tobias Golling, Moritz Kiehn, Sabrina Amrouche, Mikhail Hushchyn, Andrey Ustyuzhanin, Edward Moyse, Cécile Germain, Isabelle Guyon:
TrackML Throughput Phase. Zenodo, 2018
- [d1]Andreas Salzburger, Vincenzo Innocente, Jean-Roch Vlimant, David Rousseau, Vladimir V. Gligorov, Victor Estrade, Laurent Basara, Paolo Calafiura, Steven Farell, Heather M. Gray, Tobias Golling, Moritz Kiehn, Sabrina Amrouche, Andrey Ustyuzhanin, Mikhail Hushchyn, Edward Moyse, Cécile Germain, Isabelle Guyon:
TrackML Particle Tracking Challenge. Zenodo, 2018 - [i1]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)
Coauthor Index
aka: John Andrew Raine
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