John A. Raine

According to our database, John A. Raine authored at least 19 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.

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

Frontiers Big Data, 2024

PIPPIN: Generating variable length full events from partons.

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

CoRR, 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

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

SuperCalo: Calorimeter shower super-resolution.

Ian Pang, John Andrew Raine, David Shih

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

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

2021

Funnels: Exact maximum likelihood with dimensionality reduction.

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

CoRR, 2021