| Discovering symbolic models from deep learning with inductive biases M Cranmer, A Sanchez Gonzalez, P Battaglia, R Xu, K Cranmer, ... Advances in neural information processing systems 33, 17429-17442, 2020 | 673 | 2020 |
| Lagrangian neural networks M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho arXiv preprint arXiv:2003.04630, 2020 | 619 | 2020 |
| The CHIME fast radio burst project: system overview M Amiri, K Bandura, P Berger, M Bhardwaj, MM Boyce, PJ Boyle, C Brar, ... The Astrophysical Journal 863 (1), 48, 2018 | 385 | 2018 |
| Interpretable machine learning for science with PySR and SymbolicRegression.jl M Cranmer arXiv preprint arXiv:2305.01582, 2023 | 336 | 2023 |
| Learned coarse models for efficient turbulence simulation K Stachenfeld, DB Fielding, D Kochkov, M Cranmer, T Pfaff, J Godwin, ... arXiv preprint arXiv:2112.15275, 2021 | 148* | 2021 |
| Free-space quantum key distribution to a moving receiver JP Bourgoin, BL Higgins, N Gigov, C Holloway, CJ Pugh, S Kaiser, ... Optics express 23 (26), 33437-33447, 2015 | 123 | 2015 |
| PySR: Fast & Parallelized Symbolic Regression in Python/Julia M Cranmer http://doi.org/10.5281/zenodo.4041459, 2020 | 119* | 2020 |
| Rediscovering orbital mechanics with machine learning P Lemos, N Jeffrey, M Cranmer, S Ho, P Battaglia Machine Learning: Science and Technology 4 (4), 045002, 2023 | 111 | 2023 |
| Predicting the long-term stability of compact multiplanet systems D Tamayo, M Cranmer, S Hadden, H Rein, P Battaglia, A Obertas, ... Proceedings of the National Academy of Sciences 117 (31), 18194-18205, 2020 | 102 | 2020 |
| Learning symbolic physics with graph networks MD Cranmer, R Xu, P Battaglia, S Ho arXiv preprint arXiv:1909.05862, 2019 | 101 | 2019 |
| Multiple physics pretraining for physical surrogate models M McCabe, BRS Blancard, LH Parker, R Ohana, M Cranmer, A Bietti, ... arXiv preprint arXiv:2310.02994, 2023 | 60* | 2023 |
| xval: A continuous number encoding for large language models S Golkar, M Pettee, M Eickenberg, A Bietti, M Cranmer, G Krawezik, ... arXiv preprint arXiv:2310.02989, 2023 | 59 | 2023 |
| A deep-learning approach for live anomaly detection of extragalactic transients VA Villar, M Cranmer, E Berger, G Contardo, S Ho, G Hosseinzadeh, ... The Astrophysical Journal Supplement Series 255 (2), 24, 2021 | 53 | 2021 |
| Bifrost: A Python/C Framework for High-Throughput Stream Processing in Astronomy MD Cranmer, BR Barsdell, DC Price, J Dowell, H Garsden, V Dike, ... Journal of Astronomical Instrumentation 6 (04), 1750007, 2017 | 52 | 2017 |
| A Bayesian neural network predicts the dissolution of compact planetary systems M Cranmer, D Tamayo, H Rein, P Battaglia, S Hadden, PJ Armitage, S Ho, ... arXiv preprint arXiv:2101.04117, 2021 | 49 | 2021 |
| Mitigating radiation damage of single photon detectors for space applications E Anisimova, BL Higgins, JP Bourgoin, M Cranmer, E Choi, D Hudson, ... EPJ Quantum Technology 4, 1-14, 2017 | 46 | 2017 |
| Robust simulation-based inference in cosmology with Bayesian neural networks P Lemos, M Cranmer, M Abidi, CH Hahn, M Eickenberg, E Massara, ... Machine Learning: Science and Technology 4 (1), 01LT01, 2023 | 35 | 2023 |
| Mangrove: Learning Galaxy Properties from Merger Trees CK Jespersen, M Cranmer, P Melchior, S Ho, RS Somerville, ... The Astrophysical Journal 941 (1), 7, 2022 | 32 | 2022 |
| HIFlow: Generating diverse HI maps and inferring cosmology while marginalizing over astrophysics using normalizing flows S Hassan, F Villaescusa-Navarro, B Wandelt, DN Spergel, ... The Astrophysical Journal 937 (2), 83, 2022 | 29* | 2022 |
| Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter D Wadekar, L Thiele, F Villaescusa-Navarro, JC Hill, M Cranmer, ... Proceedings of the National Academy of Sciences 120 (12), e2202074120, 2023 | 28* | 2023 |
Shirley HoFlatiron Institute, Center for Computational AstrophysicsVerified email at flatironinstitute.org
