| Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector D de Florian, C Grojean, F Maltoni, C Mariotti, A Nikitenko, M Pieri, ... Cornell University, 2016 | 2073* | 2016 |
| The frontier of simulation-based inference K Cranmer, J Brehmer, G Louppe Proceedings of the National Academy of Sciences 117 (48), 30055-30062, 2020 | 1091 | 2020 |
| Constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review Letters 121 (11), 111801, 2018 | 207 | 2018 |
| Mining gold from implicit models to improve likelihood-free inference J Brehmer, G Louppe, J Pavez, K Cranmer Proceedings of the National Academy of Sciences, 2020, 2018 | 197 | 2018 |
| A guide to constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review D 98 (5), 052004, 2018 | 191 | 2018 |
| Flows for simultaneous manifold learning and density estimation J Brehmer, K Cranmer 34th Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 177 | 2020 |
| MadMiner: Machine learning-based inference for particle physics J Brehmer, F Kling, I Espejo, K Cranmer Computing and Software for Big Science 4 (1), 3, 2020 | 146 | 2020 |
| Pushing Higgs effective theory to its limits J Brehmer, A Freitas, D Lopez-Val, T Plehn Physical Review D 93 (7), 075014, 2016 | 139 | 2016 |
| Weakly supervised causal representation learning J Brehmer, P De Haan, P Lippe, T Cohen 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), 2022 | 138 | 2022 |
| Simulation intelligence: Towards a new generation of scientific methods A Lavin, H Zenil, B Paige, D Krakauer, J Gottschlich, T Mattson, ... arXiv preprint arXiv:2112.03235, 2021 | 132 | 2021 |
| Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning J Brehmer, S Mishra-Sharma, J Hermans, G Louppe, K Cranmer The Astrophysical Journal 886 (1), 49, 2019 | 118 | 2019 |
| Symmetry Restored in Dibosons at the LHC? J Brehmer, JA Hewett, J Kopp, T Rizzo, J Tattersall Journal of High Energy Physics 2015 (10), 1-32, 2015 | 99 | 2015 |
| Better Higgs- tests through information geometry J Brehmer, F Kling, T Plehn, TMP Tait Physical Review D 97 (9), 095017, 2018 | 85 | 2018 |
| Better Higgs boson measurements through information geometry J Brehmer, K Cranmer, F Kling, T Plehn Physical Review D 95 (7), 073002, 2017 | 84 | 2017 |
| Extending the limits of Higgs effective theory A Biekötter, J Brehmer, T Plehn Physical Review D 94 (5), 055032, 2016 | 66 | 2016 |
| Implicit neural video compression Y Zhang, T van Rozendaal, J Brehmer, M Nagel, T Cohen ICLR Workshop on Deep Generative Models for Highly Structured Data, 2022 | 59 | 2022 |
| Benchmarking simplified template cross sections in WH production J Brehmer, S Dawson, S Homiller, F Kling, T Plehn Journal of High Energy Physics 2019 (11), 1-30, 2019 | 57 | 2019 |
| Neural Message Passing for Jet Physics I Henrion, J Brehmer, J Bruna, K Cho, K Cranmer, G Louppe, G Rochette NIPS Workshop on Deep Learning for the Physical Sciences 2017, 2017 | 55* | 2017 |
| Geometric Algebra Transformer J Brehmer, P De Haan, S Behrends, T Cohen 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 52 | 2023 |
| Likelihood-free inference with an improved cross-entropy estimator M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer NeurIPS Workshop on Machine Learning for the Physical Sciences 2019, 2018 | 52 | 2018 |
Kyle CranmerUniversity of Wisconsin-MadisonE-mailová adresa ověřena na: wisc.edu
