| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2146 | 2023 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1161 | 2022 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 671* | 2024 |
| Solving quantitative reasoning problems with language models A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... Advances in Neural Information Processing Systems 35, 3843-3857, 2022 | 600 | 2022 |
| Show your work: Scratchpads for intermediate computation with language models M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ... arXiv preprint arXiv:2112.00114, 2021 | 546 | 2021 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 227 | 2023 |
| The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics G Kasieczka, B Nachman, D Shih, O Amram, A Andreassen, ... Reports on progress in physics 84 (12), 124201, 2021 | 187 | 2021 |
| Understanding the failure modes of out-of-distribution generalization V Nagarajan, A Andreassen, B Neyshabur arXiv preprint arXiv:2010.15775, 2020 | 186 | 2020 |
| Exploring length generalization in large language models C Anil, Y Wu, A Andreassen, A Lewkowycz, V Misra, V Ramasesh, ... Advances in Neural Information Processing Systems 35, 38546-38556, 2022 | 182 | 2022 |
| Scale-invariant instantons and the complete lifetime of the standard model A Andreassen, W Frost, MD Schwartz Physical Review D 97 (5), 056006, 2018 | 171 | 2018 |
| OmniFold: a method to simultaneously unfold all observables A Andreassen, PT Komiske, EM Metodiev, B Nachman, J Thaler Physical review letters 124 (18), 182001, 2020 | 165 | 2020 |
| JUNIPR: a framework for unsupervised machine learning in particle physics A Andreassen, I Feige, C Frye, MD Schwartz The European Physical Journal C 79 (2), 102, 2019 | 159 | 2019 |
| Simulation assisted likelihood-free anomaly detection A Andreassen, B Nachman, D Shih Physical Review D 101 (9), 095004, 2020 | 151 | 2020 |
| Consistent use of the standard model effective potential A Andreassen, W Frost, MD Schwartz Physical Review Letters 113 (24), 241801, 2014 | 140 | 2014 |
| Consistent use of effective potentials A Andreassen, W Frost, MD Schwartz Physical Review D 91 (1), 016009, 2015 | 132 | 2015 |
| Precision decay rate calculations in quantum field theory A Andreassen, D Farhi, W Frost, MD Schwartz Physical Review D 95 (8), 085011, 2017 | 120 | 2017 |
| Neural networks for full phase-space reweighting and parameter tuning A Andreassen, B Nachman Physical Review D 101 (9), 091901, 2020 | 101 | 2020 |
| The evolution of out-of-distribution robustness throughout fine-tuning A Andreassen, Y Bahri, B Neyshabur, R Roelofs arXiv preprint arXiv:2106.15831, 2021 | 71 | 2021 |
| Direct approach to quantum tunneling A Andreassen, D Farhi, W Frost, MD Schwartz Physical Review Letters 117 (23), 231601, 2016 | 55 | 2016 |
| Solving quantitative reasoning problems with language models, 2022 A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... URL https://arxiv. org/abs/2206.14858, 2022 | 42 | 2022 |
Matthew SchwartzHarvard UniversityVerified email at physics.harvard.edu
