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Benjamin Nachman
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2020 – today
- 2024
- [j5]C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri
, J. C. Bernauer, M. Bondì, N. Branson, T. Britton, Anja 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
Artificial Intelligence for the Electron Ion Collider (AI4EIC). Comput. Softw. Big Sci. 8(1): 5 (2024) - [i27]Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael James Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn:
The Landscape of Unfolding with Machine Learning. CoRR abs/2404.18807 (2024) - [i26]Wahid Bhimji, Paolo Calafiura, Ragansu Chakkappai, Yuan-Tang Chou, Sascha Diefenbacher, Jordan Dudley, Steven Farrell, Aishik Ghosh, Isabelle Guyon, Chris Harris, Shih-Chieh Hsu, Elham E Khoda, Rémy Lyscar, Alexandre Michon, Benjamin Nachman, Peter Nugent, Mathis Reymond, David Rousseau, Benjamin Sluijter, Benjamin Thorne, Ihsan Ullah, Yulei Zhang:
FAIR Universe HiggsML Uncertainty Challenge Competition. CoRR abs/2410.02867 (2024) - [i25]Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang:
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. CoRR abs/2410.21611 (2024) - 2023
- [i24]Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley
Weakly-Supervised Anomaly Detection in the Milky Way. CoRR abs/2305.03761 (2023) - [i23]Vinicius Mikuni, Benjamin Nachman:
High-dimensional and Permutation Invariant Anomaly Detection. CoRR abs/2306.03933 (2023) - [i22]Fernando Torales Acosta, Vinicius Mikuni, Benjamin Nachman, Miguel Arratia, Bishnu Karki, Ryan Milton, Piyush Karande
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation. CoRR abs/2307.04780 (2023) - [i21]C. Allaire, R. Ammendola, E.-C. Aschenauer, M. Balandat, Marco Battaglieri, J. C. Bernauer, M. Bondì, N. Branson, Thomas Britton, Anja 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
Artificial Intelligence for the Electron Ion Collider (AI4EIC). CoRR abs/2307.08593 (2023) - [i20]Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni, Benjamin Nachman, Weili Nie:
Improving Generative Model-based Unfolding with Schrödinger Bridges. CoRR abs/2308.12351 (2023) - [i19]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) - [i18]Fernando Torales Acosta, Bishnu Karki, Piyush Karande, Aaron Angerami, Miguel Arratia, Kenneth Barish, Ryan Milton, Sebastián Morán, Benjamin Nachman, Anshuman Sinha:
The Optimal use of Segmentation for Sampling Calorimeters. CoRR abs/2310.04442 (2023) - [i17]Owen Long, Benjamin Nachman:
Designing Observables for Measurements with Deep Learning. CoRR abs/2310.08717 (2023) - 2022
- [j4]Benjamin Nachman:
When, Where, and How to Open Data: a Personal Perspective. Comput. Softw. Big Sci. 6(1): 17 (2022) - [j3]Wonho Jang
Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer. Quantum 6: 798 (2022) - [i16]Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation. CoRR abs/2203.08806 (2022) - [i15]Kingman Cheung, Yi-Lun Chung, Shih-Chieh Hsu, Benjamin Nachman:
Exploring the Universality of Hadronic Jet Classification. CoRR abs/2204.03812 (2022) - [i14]Vinicius Mikuni, Benjamin Nachman:
Score-based Generative Models for Calorimeter Shower Simulation. CoRR abs/2206.11898 (2022) - [i13]Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson:
Machine-Learning Compression for Particle Physics Discoveries. CoRR abs/2210.11489 (2022) - [i12]Mayee F. Chen, Benjamin Nachman, Frederic Sala:
Resonant Anomaly Detection with Multiple Reference Datasets. CoRR abs/2212.10579 (2022) - 2021
- [i11]Matthew Feickert
A Living Review of Machine Learning for Particle Physics. CoRR abs/2102.02770 (2021) - [i10]Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Adi Suresh, Jesse Thaler:
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution. CoRR abs/2105.04448 (2021) - [i9]Ramon Winterhalder
Latent Space Refinement for Deep Generative Models. CoRR abs/2106.00792 (2021) - [i8]Gregor Kasieczka, Benjamin Nachman, David Shih
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics. CoRR abs/2107.02821 (2021) - [i7]Aishik Ghosh, Benjamin Nachman:
A Cautionary Tale of Decorrelating Theory Uncertainties. CoRR abs/2109.08159 (2021) - [i6]Vinicius Mikuni, Benjamin Nachman, David Shih
Online-compatible Unsupervised Non-resonant Anomaly Detection. CoRR abs/2111.06417 (2021) - [i5]Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert
Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR abs/2111.13786 (2021)
2010 – 2019
- 2019
- [i4]Benjamin Nachman, Chase Shimmin:
AI Safety for High Energy Physics. CoRR abs/1910.08606 (2019) - 2017
- [j2]Luke de Oliveira
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis. Comput. Softw. Big Sci. 1(1) (2017) - [i3]Luke de Oliveira, Michela Paganini, Benjamin Nachman:
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters. CoRR abs/1711.08813 (2017) - [i2]Michela Paganini, Luke de Oliveira, Benjamin Nachman:
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. CoRR abs/1712.10321 (2017) - 2016
- [j1]Hyeji Kim, Benjamin Nachman, Abbas El Gamal:
Superposition Coding Is Almost Always Optimal for the Poisson Broadcast Channel. IEEE Trans. Inf. Theory 62(4): 1782-1794 (2016) - 2015
- [c1]Hyeji Kim, Benjamin Nachman, Abbas El Gamal:
Superposition coding is almost always optimal for the Poisson broadcast channel. ISIT 2015: 834-838 - [i1]Hyeji Kim, Benjamin Nachman, Abbas El Gamal:
Superposition Coding is Almost Always Optimal for the Poisson Broadcast Channel. CoRR abs/1508.04228 (2015)
Coauthor Index
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last updated on 2024-12-03 20:31 CET by the dblp team
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