Miles D. Cranmer

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Symbolic Regression

Machine Learning

Top Venues

Mach. Learn. Sci. Technol.

Preregister@NeurIPS

Publications

Siavash Golkar, Alberto Bietti, Mariel Pettee, Michael Eickenberg, Miles D. Cranmer, Keiya Hirashima, Géraud Krawezik, Nicholas Lourie, Michael McCabe, Rudy Morel, Ruben Ohana, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR (2024)
Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Géraud Krawezik, François Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
Multiple Physics Pretraining for Physical Surrogate Models. CoRR (2023)
Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles D. Cranmer, Géraud Krawezik, François Lanusse, Michael McCabe, Ruben Ohana, Liam Holden Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
xVal: A Continuous Number Encoding for Large Language Models. CoRR (2023)
Miles D. Cranmer
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. CoRR (2023)
Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. 4 (4) (2023)
François Lanusse, Liam Holden Parker, Siavash Golkar, Miles D. Cranmer, Alberto Bietti, Michael Eickenberg, Géraud Krawezik, Michael McCabe, Ruben Ohana, Mariel Pettee, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR (2023)
Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, M. S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, B. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, G. Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR (2023)
Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
Robust simulation-based inference in cosmology with Bayesian neural networks. Mach. Learn. Sci. Technol. 4 (1) (2023)
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles D. Cranmer, Sridhara Dasu, Peter Elmer, Philip C. Harris, Isobel Ojalvo, Maurizio Pierini
Symbolic Regression on FPGAs for Fast Machine Learning Inference. CoRR (2023)
Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR (2023)
Thomas Pfeil, Miles D. Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr
A Neural Network Subgrid Model of the Early Stages of Planet Formation. CoRR (2022)
Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, David N. Spergel, Miles D. Cranmer, Nicholas Battaglia, Daniel Anglés-Alcázar, Lars Hernquist, Shirley Ho
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. CoRR (2022)
Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
Mangrove: Learning Galaxy Properties from Merger Trees. CoRR (2022)
Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess Smidt, Shirley Ho
Learning Integrable Dynamics with Action-Angle Networks. CoRR (2022)
Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles D. Cranmer, Daisuke Nagai, Daniel Anglés-Alcázar, Shirley Ho, Lars Hernquist
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. CoRR (2022)
Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
Rediscovering orbital mechanics with machine learning. CoRR (2022)
Kimberly Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez
Learned Simulators for Turbulence. ICLR (2022)
Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Mach. Learn. Sci. Technol. 3 (3) (2022)
Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks. CoRR (2022)
Kaze W. K. Wong, Miles D. Cranmer
Automated discovery of interpretable gravitational-wave population models. CoRR (2022)
Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks. CoRR (2022)
Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel
A Bayesian neural network predicts the dissolution of compact planetary systems. CoRR (2021)
Kimberly L. Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez
Learned Coarse Models for Efficient Turbulence Simulation. CoRR (2021)
Miles D. Cranmer, Peter Melchior, Brian Nord
Unsupervised Resource Allocation with Graph Neural Networks. CoRR (2021)
V. Ashley Villar, Miles D. Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. CoRR (2021)
Miles D. Cranmer, Peter Melchior, Brian Nord
Unsupervised Resource Allocation with Graph Neural Networks. Preregister@NeurIPS (2020)
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS (2020)
Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo
Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. CoRR (2020)
V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin
Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR (2020)
Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho
Lagrangian Neural Networks. CoRR (2020)
Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR (2020)
Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho
Learning Symbolic Physics with Graph Networks. CoRR (2019)
Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. CoRR (2019)
Miles D. Cranmer, Benjamin R. Barsdell, Danny C. Price, Jayce Dowell, Hugh Garsden, Veronica Dike, Tarraneh Eftekhari, Alexander M. Hegedus, Joseph Malins, Kenneth S. Obenberger, Frank Schinzel, Kevin Stovall, Gregory B. Taylor, Lincoln J. Greenhill
Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy. CoRR (2017)