Miles Cranmer
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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
Lagrangian neural networks
M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho
arXiv preprint arXiv:2003.04630, 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
Interpretable machine learning for science with PySR and SymbolicRegression.jl
M Cranmer
arXiv preprint arXiv:2305.01582, 2023
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
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
PySR: Fast & Parallelized Symbolic Regression in Python/Julia
M Cranmer, 2020
Learning symbolic physics with graph networks
MD Cranmer, R Xu, P Battaglia, S Ho
arXiv preprint arXiv:1909.05862, 2019
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
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
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
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
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
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
Mangrove: Learning Galaxy Properties from Merger Trees
CK Jespersen, M Cranmer, P Melchior, S Ho, RS Somerville, ...
The Astrophysical Journal 941 (1), 7, 2022
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
The SZ flux-mass (YM) relation at low-halo masses: improvements with symbolic regression and strong constraints on baryonic feedback
D Wadekar, L Thiele, JC Hill, S Pandey, F Villaescusa-Navarro, ...
Monthly Notices of the Royal Astronomical Society 522 (2), 2628-2643, 2023
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
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
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
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Articles 1–20