Tobias Golling

According to our database, Tobias Golling authored at least 27 papers between 2018 and 2024.

Collaborative distances:

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

2024

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models.

Lukas Heinrich, Tobias Golling, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine

CoRR, 2024

2023

The Tracking Machine Learning Challenge: Throughput Phase.

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

Comput. Softw. Big Sci., December, 2023

Improving new physics searches with diffusion models for event observables and jet constituents.

Debajyoti Sengupta, Matthew Leigh, John Andrew Raine, Samuel Klein, Tobias Golling

CoRR, 2023

EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion.

Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih

CoRR, 2023

Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation.

Tobias Golling, Samuel Klein, Radha Mastandrea, Benjamin Nachman, John Andrew Raine

CoRR, 2023

PC-Droid: Faster diffusion and improved quality for particle cloud generation.

Matthew Leigh, Debajyoti Sengupta, John Andrew Raine, Guillaume Quétant, Tobias Golling

CoRR, 2023

Decorrelation using Optimal Transport.

Malte Algren, John Andrew Raine, Tobias Golling

CoRR, 2023

ν²-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows.

John Andrew Raine, Matthew Leigh, Knut Zoch, Tobias Golling

CoRR, 2023

CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation.

Debajyoti Sengupta, Samuel Klein, John Andrew Raine, Tobias Golling

CoRR, 2023

Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting.

Malte Algren, Tobias Golling, Manuel Guth, Chris Pollard, John Andrew Raine

CoRR, 2023

Topological Reconstruction of Particle Physics Processes using Graph Neural Networks.

Lukas Ehrke, John Andrew Raine, Knut Zoch, Manuel Guth, Tobias Golling

CoRR, 2023

PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics.

Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant, John Andrew Raine, Knut Zoch, Tobias Golling

CoRR, 2023

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics.

Atul Kumar Sinha, Daniele Paliotta, Bálint Máté, John A. Raine, Tobias Golling, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation.

Samuel Klein, John Andrew Raine, Tobias Golling

CoRR, 2022

Decorrelation with conditional normalizing flows.

Samuel Klein, Tobias Golling

CoRR, 2022

ν-Flows: Conditional Neutrino Regression.

Matthew Leigh, John Andrew Raine, Knut Zoch, Tobias Golling

CoRR, 2022

SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics.

Atul Kumar Sinha, Daniele Paliotta, Bálint Máté, Sebastian Pina-Otey, John A. Raine, Tobias Golling, François Fleuret

CoRR, 2022

Flowification: Everything is a normalizing flow.

Bálint Máté, Samuel Klein, Tobias Golling, François Fleuret

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Turbo-Sim: a generalised generative model with a physical latent space.

Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh, Tobias Golling, Slava Voloshynovskiy

CoRR, 2021

Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN.

Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy

CoRR, 2021

Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks.

Mariia Drozdova, Vitaliy Kinakh, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy

CoRR, 2021

Funnels: Exact maximum likelihood with dimensionality reduction.

Samuel Klein, John A. Raine, Sebastian Pina-Otey, Slava Voloshynovskiy, Tobias Golling

CoRR, 2021

Hashing and metric learning for charged particle tracking.

Sabrina Amrouche, Moritz Kiehn, Tobias Golling, Andreas Salzburger

CoRR, 2021

2019

Similarity hashing for charged particle tracking.

Sabrina Amrouche, Tobias Golling, Moritz Kiehn, Claudia Plant, Andreas Salzburger

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

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

Deep Generative Models for Fast Shower Simulation in ATLAS.

Dalila Salamani, Stefan Gadatsch, Tobias Golling, Graeme Andrew Stewart, Aishik Ghosh, David Rousseau, Ahmed Hasib, Jana Schaarschmidt

Proceedings of the 14th IEEE International Conference on e-Science, 2018

TrackML: A High Energy Physics Particle Tracking Challenge.

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

Proceedings of the 14th IEEE International Conference on e-Science, 2018