default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMiles D. Cranmer
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[i31]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 abs/2406.02585 (2024)- [i30]Caleb Lammers, Miles D. Cranmer, Samuel Hadden, Shirley Ho, Norman Murray, Daniel Tamayo:
Accelerating Giant Impact Simulations with Machine Learning. CoRR abs/2408.08873 (2024) - 2023
- [j3]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): 01 (2023) - [j2]Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia:
Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. 4(4): 45002 (2023) - [i29]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:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - [i28]Miles D. Cranmer:
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. CoRR abs/2305.01582 (2023) - [i27]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 abs/2305.04099 (2023) - [i26]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 abs/2309.16645 (2023) - [i25]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 abs/2310.02989 (2023) - [i24]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 abs/2310.02994 (2023) - [i23]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 abs/2310.03024 (2023) - 2022
- [j1]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): 35002 (2022) - [c3]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:
Learned Simulators for Turbulence. ICLR 2022 - [i22]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 abs/2201.01305 (2022) - [i21]Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia:
Rediscovering orbital mechanics with machine learning. CoRR abs/2202.02306 (2022) - [i20]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 abs/2203.00026 (2022) - [i19]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 abs/2207.08435 (2022) - [i18]Kaze W. K. Wong, Miles D. Cranmer:
Automated discovery of interpretable gravitational-wave population models. CoRR abs/2207.12409 (2022) - [i17]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 abs/2209.02075 (2022) - [i16]Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai:
Mangrove: Learning Galaxy Properties from Merger Trees. CoRR abs/2210.13473 (2022) - [i15]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 abs/2211.04160 (2022) - [i14]Ji Won Park, Simon Birrer, Madison Ueland, Miles D. Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman:
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. CoRR abs/2211.07807 (2022) - [i13]David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré:
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR abs/2211.09008 (2022) - [i12]Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho:
Learning Integrable Dynamics with Action-Angle Networks. CoRR abs/2211.15338 (2022) - 2021
- [i11]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 abs/2101.04117 (2021) - [i10]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 abs/2103.12102 (2021) - [i9]Miles D. Cranmer, Peter Melchior, Brian Nord:
Unsupervised Resource Allocation with Graph Neural Networks. CoRR abs/2106.09761 (2021) - [i8]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 abs/2112.15275 (2021) - 2020
- [c2]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 - [c1]Miles D. Cranmer, Peter Melchior, Brian Nord:
Unsupervised Resource Allocation with Graph Neural Networks. Preregister@NeurIPS 2020: 272-284 - [i7]Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho:
Lagrangian Neural Networks. CoRR abs/2003.04630 (2020) - [i6]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 abs/2006.11287 (2020) - [i5]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 abs/2007.04459 (2020) - [i4]V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin:
Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR abs/2010.11194 (2020)
2010 – 2019
- 2019
- [i3]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 abs/1908.08045 (2019) - [i2]Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho:
Learning Symbolic Physics with Graph Networks. CoRR abs/1909.05862 (2019) - 2017
- [i1]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 abs/1708.00720 (2017)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:22 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
