Shirley Ho

According to our database, Shirley Ho authored at least 49 papers between 2015 and 2024.

Collaborative distances:

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

2024

ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback.

Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junnichiro Makino, Ulrich P. Steinwandel, Shirley Ho

CoRR, 2024

Accelerating Giant Impact Simulations with Machine Learning.

Caleb Lammers, Miles D. Cranmer, Samuel Hadden, Shirley Ho, Norman Murray, Daniel Tamayo

CoRR, 2024

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task.

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

CoRR, 2024

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

Scientific discovery in the age of artificial intelligence.

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik

Nat., 2023

Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations.

Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino, Shirley Ho

CoRR, 2023

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering.

Pablo Lemos, Liam Holden Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, David N. Spergel

CoRR, 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

Learnable wavelet neural networks for cosmological inference.

Christian Pedersen, Michael Eickenberg, Shirley Ho

CoRR, 2023

Predicting the Initial Conditions of the Universe using Deep Learning.

Vaibhav Jindal, Drew Jamieson, Albert Liang, Aarti Singh, Shirley Ho

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

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

Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning.

Yan-Mong Chan, Natascha Manger, Yin Li, Chao-Chin Yang, Zhaohuan Zhu, Philip J. Armitage, Shirley Ho

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

Field Level Neural Network Emulator for Cosmological N-body Simulations.

Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel

CoRR, 2022

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation.

Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho, Renan Alves de Oliveira, David N. Spergel

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

Super-resolving Dark Matter Halos using Generative Deep Learning.

David Schaurecker, Yin Li, Jeremy Tinker, Shirley Ho, Alexandre Refregier

CoRR, 2021

Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes.

Ji Won Park, V. Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman

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

Learning the Evolution of the Universe in N-body Simulations.

Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, Anthony Pullen

CoRR, 2020

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning.

Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel

CoRR, 2020

deep21: a Deep Learning Method for 21cm Foreground Removal.

T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault Levasseur, David N. Spergel

CoRR, 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

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

From Dark Matter to Galaxies with Convolutional Neural Networks.

Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho

CoRR, 2019

Learning neutrino effects in Cosmology with Convolutional Neural Networks.

Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro, Siyu He, Shirley Ho, ChangHoon Hahn

CoRR, 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

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks.

Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro, Seda Bilaloglu, Asena Derin Cengiz, Siyu He, Laurence Perreault Levasseur, Shirley Ho

CoRR, 2019

From Dark Matter to Galaxies with Convolutional Networks.

Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho

CoRR, 2019

2018

Learning to Predict the Cosmological Structure Formation.

Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos

CoRR, 2018

CosmoFlow: using deep learning to learn the universe at scale.

Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Kärnä, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee

Proceedings of the International Conference for High Performance Computing, 2018

Analysis of Cosmic Microwave Background with Deep Learning.

Siyu He, Siamak Ravanbakhsh, Shirley Ho

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Estimating Cosmological Parameters from the Dark Matter Distribution.

Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Optimal Ridge Detection using Coverage Risk.

Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Finding Galaxies in the Shadows of Quasars with Gaussian Processes.

Roman Garnett, Shirley Ho, Jeff G. Schneider

Proceedings of the 32nd International Conference on Machine Learning, 2015

Fast Function to Function Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015