| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1042 | 2023 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 862 | 2021 |
| Improving language models by retrieving from trillions of tokens S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ... International conference on machine learning, 2206-2240, 2022 | 796 | 2022 |
| Optimisation and performance studies of the ATLAS b-tagging algorithms for the 2017-18 LHC run Atlas Collaboration ATL-PHYS-PUB-2017-013, 2017 | 600 | 2017 |
| CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks M Paganini, L de Oliveira, B Nachman Physical Review D 97 (1), 014021, 2018 | 400 | 2018 |
| Learning particle physics by example: location-aware generative adversarial networks for physics synthesis L de Oliveira, M Paganini, B Nachman Computing and Software for Big Science 1 (1), 4, 2017 | 325 | 2017 |
| Machine learning in high energy physics community white paper K Albertsson, P Altoe, D Anderson, J Anderson, M Andrews, ... arXiv preprint arXiv:1807.02876, 2018 | 289 | 2018 |
| Accelerating science with generative adversarial networks: an application to 3D particle showers in multilayer calorimeters M Paganini, L de Oliveira, B Nachman Physical review letters 120 (4), 042003, 2018 | 282 | 2018 |
| Measurements of Higgs boson properties in the diphoton decay channel with of collision data at with the ATLAS detector M Aaboud, G Aad, B Abbott, B Abeloos, SH Abidi, OS AbouZeid, ... Physical Review D 98 (5), 052005, 2018 | 256* | 2018 |
| One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers A Morcos, H Yu, M Paganini, Y Tian Advances in Neural Information Processing Systems, 4932-4942, 2019 | 241 | 2019 |
| Search for Higgs boson pair production in the yybb final state with 13 TeV pp collision data collected by the ATLAS experiment A collaboration Journal of High Energy Physics 2018 (11), 40, 2018 | 238* | 2018 |
| Identification of jets containing b-hadrons with recurrent neural networks at the ATLAS experiment ATLAS collaboration ATLAS note: ATL-PHYS-PUB-2017-003, http://cds. cern. ch/record/2255226, 2017 | 211 | 2017 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 196 | 2024 |
| A Roadmap for HEP Software and Computing R&D for the 2020s J Albrecht, AA Alves, G Amadio, G Andronico, N Anh-Ky, L Aphecetche, ... Computing and software for big science 3 (1), 1-49, 2019 | 176 | 2019 |
| Controlling physical attributes in gan-accelerated simulation of electromagnetic calorimeters L de Oliveira, M Paganini, B Nachman Journal of Physics: Conference Series 1085 (4), 042017, 2018 | 70 | 2018 |
| Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC W Bhimji, SA Farrell, T Kurth, M Paganini, E Racah arXiv preprint arXiv:1711.03573, 2017 | 58 | 2017 |
| Unified Scaling Laws for Routed Language Models A Clark, D Casas, A Guy, A Mensch, M Paganini, J Hoffmann, B Damoc, ... arXiv preprint arXiv:2202.01169, 2022 | 44 | 2022 |
| Search for Higgs boson pair production in the bbγγ final state using pp collision data at√ s= 13 TeV with the ATLAS detector ATLAS collaboration ATLAS-CONF-2016-004, 2016 | 42 | 2016 |
| The scientific method in the science of machine learning JZ Forde, M Paganini arXiv preprint arXiv:1904.10922, 2019 | 40 | 2019 |
| Electromagnetic showers beyond shower shapes L De Oliveira, B Nachman, M Paganini Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2020 | 38 | 2020 |
