Miles D. Cranmer

According to our database, Miles D. Cranmer authored at least 30 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Rediscovering orbital mechanics with machine learning.

Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia

Mach. Learn. Sci. Technol., December, 2023

Robust simulation-based inference in cosmology with Bayesian neural networks.

Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho

Mach. Learn. Sci. Technol., March, 2023

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models.

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

CoRR, 2023

Multiple Physics Pretraining for Physical Surrogate Models.

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

CoRR, 2023

xVal: A Continuous Number Encoding for Large Language Models.

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

CoRR, 2023

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures.

Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho

CoRR, 2023

Symbolic Regression on FPGAs for Fast Machine Learning Inference.

Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles D. Cranmer, Sridhara Dasu, Peter Elmer, Philip C. Harris, Isobel Ojalvo, Maurizio Pierini

CoRR, 2023

Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl.

Miles D. Cranmer

CoRR, 2023

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition.

Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, C. S. Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, A. Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava

CoRR, 2023

2022

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks.

Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel

Mach. Learn. Sci. Technol., 2022

Learning Integrable Dynamics with Action-Angle Networks.

Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho

CoRR, 2022

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study.

David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré

CoRR, 2022

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks.

Ji Won Park, Simon Birrer, Madison Ueland, Miles D. Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman

CoRR, 2022

A Neural Network Subgrid Model of the Early Stages of Planet Formation.

Thomas Pfeil, Miles D. Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr

CoRR, 2022

Mangrove: Learning Galaxy Properties from Merger Trees.

Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai

CoRR, 2022

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback.

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

CoRR, 2022

Automated discovery of interpretable gravitational-wave population models.

Kaze W. K. Wong, Miles D. Cranmer

CoRR, 2022

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter.

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

CoRR, 2022

Learned Simulators for Turbulence.

Kimberly L. Stachenfeld, Drummond Buschman Fielding, Dmitrii Kochkov, Miles D. Cranmer, Tobias Pfaff, Jonathan Godwin, Can Cui, Shirley Ho, Peter W. Battaglia, Alvaro Sanchez-Gonzalez

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Learned Coarse Models for Efficient Turbulence Simulation.

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

CoRR, 2021

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients.

V. Ashley Villar, Miles D. Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin

CoRR, 2021

A Bayesian neural network predicts the dissolution of compact planetary systems.

Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel

CoRR, 2021

2020

Anomaly Detection for Multivariate Time Series of Exotic Supernovae.

V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin

CoRR, 2020

Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way.

Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo

CoRR, 2020

Lagrangian Neural Networks.

Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho

CoRR, 2020

Unsupervised Resource Allocation with Graph Neural Networks.

Miles D. Cranmer, Peter Melchior, Brian Nord

Proceedings of the NeurIPS 2020 Workshop on Pre-registration in Machine Learning, 2020

Discovering Symbolic Models from Deep Learning with Inductive Biases.

Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Learning Symbolic Physics with Graph Networks.

Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho

CoRR, 2019

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates.

Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho

CoRR, 2019

2017

Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy.

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

CoRR, 2017