Daniel Whiteson

According to our database, Daniel Whiteson authored at least 19 papers between 2007 and 2024.

Collaborative distances:

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

2024

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, T. Britton, A. Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, E. Fol, S. Furletov, Y. Gao, James Giroux, N. C. Gunawardhana Waduge, O. Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, A. Jalotra, D. Jayakodige, B. Joo, M. Junaid, N. Kalantarians, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, Y. Li, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, M. L. Purschke, H. Rajendran, K. Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, A. Saini, N. Sato, T. Satogata, German Sborlini, Malachi Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, J. Stevens, P. Stone, L. Suarez, K. Suresh, A.-N. Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, Anselm Vossen, E. Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

Comput. Softw. Big Sci., December, 2024

2023

Extended Symmetry Preserving Attention Networks for LHC Analysis.

Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

CoRR, 2023

Artificial Intelligence for the Electron Ion Collider (AI4EIC).

C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, A. Butter, I. Chahrour, P. Chatagnon, Evaristo Cisbani, E. W. Cline, S. Dash, C. T. Dean, W. Deconinck, A. Deshpande, Markus Diefenthaler, R. Ent, Cristiano Fanelli, M. Finger, M. Finger Jr., E. Fol, S. Furletov, Yuan Gao, James Giroux, N. C. Gunawardhana Waduge, R. Harish, O. Hassan, P. L. Hegde, Roger José Hernández-Pinto, Astrid N. Hiller Blin, Tanja Horn, J. Huang, D. Jayakodige, B. Joo, M. Junaid, Piyush Karande, B. Kriesten, R. Kunnawalkam Elayavalli, M. Lin, Frank Liu, S. Liuti, G. Matousek, Matthew McEneaney, Diana McSpadden, T. Menzo, T. Miceli, Vinicius Mikuni, R. Montgomery, Benjamin Nachman, R. R. Nair, J. Niestroy, S. A. Ochoa Oregon, J. Oleniacz, J. D. Osborn, C. Paudel, C. Pecar, C. Peng, Gabriel N. Perdue, W. Phelps, M. L. Purschke, K. Rajput, Y. Ren, David Francisco Rentería-Estrada, D. Richford, B. J. Roy, D. Roy, N. Sato, T. Satogata, German Sborlini, Malachi Schram, D. Shih, J. Singh, R. Singh, A. Siodmok, P. Stone, J. Stevens, L. Suarez, K. Suresh, A.-N. Tawfik, Fernando Torales Acosta, N. Tran, R. Trotta, F. J. Twagirayezu, R. Tyson, S. Volkova, Anselm Vossen, E. Walter, Daniel Whiteson, Michael Williams, S. Wu, N. Zachariou, P. Zurita

CoRR, 2023

Generalizing to new calorimeter geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation.

Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson

CoRR, 2023

End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics.

Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

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

2022

Geometry-aware Autoregressive Models for Calorimeter Shower Simulations.

Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson

CoRR, 2022

Machine-Learning Compression for Particle Physics Discoveries.

Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson

CoRR, 2022

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning.

Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson

CoRR, 2022

2021

SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention.

Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

CoRR, 2021

2020

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks.

Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

CoRR, 2020

2016

HEPMASS.

Daniel Whiteson

Dataset, January, 2016

Parameterized Machine Learning for High-Energy Physics.

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter J. Sadowski, Daniel Whiteson

CoRR, 2016

2014

SUSY.

Daniel Whiteson

Dataset, February, 2014

HIGGS.

Daniel Whiteson

Dataset, February, 2014

Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning.

Pierre Baldi, Peter J. Sadowski, Daniel Whiteson

CoRR, 2014

Searching for Higgs Boson Decay Modes with Deep Learning.

Peter J. Sadowski, Daniel Whiteson, Pierre Baldi

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Deep Learning, Dark Knowledge, and Dark Matter.

Peter J. Sadowski, Julian Collado, Daniel Whiteson, Pierre Baldi

Proceedings of the Workshop on High-energy Physics and Machine Learning, 2014

2009

Machine learning for event selection in high energy physics.

Shimon Whiteson, Daniel Whiteson

Eng. Appl. Artif. Intell., 2009

2007

Stochastic Optimization for Collision Selection in High Energy Physics.

Shimon Whiteson, Daniel Whiteson

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007