Shirley Ho - RankMe

69 papers A* 7A 2Journal 57Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	On the Value of Tokeniser Pretraining in Physics Foundation Models. CoRR Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana, Michael McCabe, Neil D. Lawrence, Shirley Ho, Miles D. Cranmer
2026	J	`jnl`	Representation Learning for Spatiotemporal Physical Systems. CoRR Helen Qu, Rudy Morel, Michael McCabe, Alberto Bietti, François Lanusse, Shirley Ho, Yann LeCun
2026	J	`jnl`	Test-time Generalization for Physics through Neural Operator Splitting. CoRR Louis Serrano, Jiequn Han, Edouard Oyallon, Shirley Ho, Rudy Morel
2025	J	`jnl`	Accelerating scientific discovery with the common task framework. CoRR J. Nathan Kutz, Peter W. Battaglia, Michael P. Brenner, Kevin Carlberg, Aric A. Hagberg, Shirley Ho, Stephan Hoyer, Henning Lange, Hod Lipson, Michael W. Mahoney, Frank Noé, Max Welling, Laure Zanna, Francis Zhu, Steven L. Brunton
2025	J	`jnl`	Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation. CoRR François Rozet, Ruben Ohana, Michael McCabe, Gilles Louppe, François Lanusse, Shirley Ho
2025	J	`jnl`	Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme. CoRR Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti, Kyunghyun Cho, Miles D. Cranmer, Siavash Golkar, Olexandr Gugnin, Géraud Krawezik, Tanya Marwah, Michael McCabe, Lucas Meyer, Payel Mukhopadhyay, Ruben Ohana, Liam Holden Parker, Helen Qu, François Rozet, K. D. Leka, François Lanusse, David Fouhey, Shirley Ho
2025	J	`jnl`	Semantic search for 100M+ galaxy images using AI-generated captions. CoRR Nolan Koblischke, Liam Holden Parker, François Lanusse, Irina Espejo Morales, Jo Bovy, Shirley Ho
2025	J	`jnl`	Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning. CoRR Jeff Shen, François Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Nathan Cassereau, Pierre Cornette, Keiya Hirashima, Géraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2025	J	`jnl`	Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. CoRR Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze W. K. Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Géraud Krawezik, François Lanusse, Rudy Morel, Ruben Ohana, Liam Holden Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2024	J	`jnl`	ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback. CoRR Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junnichiro Makino, Ulrich P. Steinwandel, Shirley Ho
2024	J	`jnl`	Accelerating Giant Impact Simulations with Machine Learning. CoRR Caleb Lammers, Miles D. Cranmer, Samuel Hadden, Shirley Ho, Norman Murray, Daniel Tamayo
2024	J	`jnl`	Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR 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
2024	A*	`conf`	Multiple Physics Pretraining for Spatiotemporal Surrogate Models. NeurIPS 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
2024	—	`conf`	Point Clouds with Geometric and Semantic Confidence Intervals. ICPRAI (2) Victor Sim, Shirley Ho
2024	A*	`conf`	The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. NeurIPS Eirini Angeloudi, Jeroen Audenaert, Micah Bowles, Benjamin M. Boyd, David Chemaly, Brian Cherinka, Ioana Ciuca, Miles D. Cranmer, Aaron Do, Matthew Grayling, Erin E. Hayes, Tom Hehir, Shirley Ho, Marc Huertas-Company, Kartheik Iyer, Maja Jablonska, François Lanusse, Henry Leung, Kaisey Mandel, Rafael Martínez-Galarza, Peter Melchior, Lucas Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F. Wu
2024	A*	`conf`	The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. NeurIPS Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho
2024	J	`jnl`	The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. CoRR Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho
2023	J	`jnl`	AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR 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
2023	J	`jnl`	Learnable wavelet neural networks for cosmological inference. CoRR Christian Pedersen, Michael Eickenberg, Shirley Ho
2023	J	`jnl`	Multiple Physics Pretraining for Physical Surrogate Models. CoRR 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
2023	J	`jnl`	Predicting the Initial Conditions of the Universe using Deep Learning. CoRR Vaibhav Jindal, Drew Jamieson, Albert Liang, Aarti Singh, Shirley Ho
2023	J	`jnl`	Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
2023	J	`jnl`	Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho
2023	J	`jnl`	Robust simulation-based inference in cosmology with Bayesian neural networks. Mach. Learn. Sci. Technol. Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
2023	J	`jnl`	Scientific discovery in the age of artificial intelligence. Nat. 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 K. 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
2023	J	`jnl`	SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering. CoRR 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
2023	J	`jnl`	Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations. CoRR Keiya Hirashima, Kana Moriwaki, Michiko S. Fujii, Yutaka Hirai, Takayuki R. Saitoh, Junichiro Makino, Shirley Ho
2023	J	`jnl`	xVal: A Continuous Number Encoding for Large Language Models. CoRR 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
2022	J	`jnl`	A Neural Network Subgrid Model of the Early Stages of Planet Formation. CoRR Thomas Pfeil, Miles D. Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr
2022	J	`jnl`	Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. CoRR 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
2022	J	`jnl`	Field Level Neural Network Emulator for Cosmological N-body Simulations. CoRR Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel
2022	A*	`conf`	Learned Simulators for Turbulence. ICLR 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
2022	J	`jnl`	Learning Integrable Dynamics with Action-Angle Networks. CoRR Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho
2022	J	`jnl`	Mangrove: Learning Galaxy Properties from Merger Trees. CoRR Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
2022	J	`jnl`	Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning. CoRR Yan-Mong Chan, Natascha Manger, Yin Li, Chao-Chin Yang, Zhaohuan Zhu, Philip J. Armitage, Shirley Ho
2022	J	`jnl`	Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks. CoRR Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
2022	J	`jnl`	Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Mach. Learn. Sci. Technol. Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
2022	J	`jnl`	Rediscovering orbital mechanics with machine learning. CoRR Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
2022	J	`jnl`	Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks. CoRR Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
2022	J	`jnl`	Simple lessons from complex learning: what a neural network model learns about cosmic structure formation. CoRR Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho, Renan Alves de Oliveira, David N. Spergel
2022	J	`jnl`	The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. CoRR 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
2021	J	`jnl`	A Bayesian neural network predicts the dissolution of compact planetary systems. CoRR Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel
2021	J	`jnl`	A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. CoRR V. Ashley Villar, Miles D. Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
2021	J	`jnl`	Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes. CoRR Ji Won Park, V. Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman
2021	J	`jnl`	Learned Coarse Models for Efficient Turbulence Simulation. CoRR 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
2021	J	`jnl`	Super-resolving Dark Matter Halos using Generative Deep Learning. CoRR David Schaurecker, Yin Li, Jeremy Tinker, Shirley Ho, Alexandre Refregier
2020	J	`jnl`	Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin
2020	A*	`conf`	Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020	J	`jnl`	Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020	J	`jnl`	Fast and Accurate Non-Linear Predictions of Universes with Deep Learning. CoRR Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, David N. Spergel
2020	J	`jnl`	Lagrangian Neural Networks. CoRR Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho
2020	J	`jnl`	Learning the Evolution of the Universe in N-body Simulations. CoRR Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho, Anthony Pullen
2020	J	`jnl`	Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. CoRR Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo
2020	J	`jnl`	deep21: a Deep Learning Method for 21cm Foreground Removal. CoRR T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro, Peter Melchior, Shirley Ho, Laurence Perreault Levasseur, David N. Spergel
2019	J	`jnl`	From Dark Matter to Galaxies with Convolutional Networks. CoRR Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro, Shirley Ho
2019	J	`jnl`	From Dark Matter to Galaxies with Convolutional Neural Networks. CoRR Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro, Siyu He, Shy Genel, Shirley Ho
2019	J	`jnl`	HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks. CoRR Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro, Seda Bilaloglu, Asena Derin Cengiz, Siyu He, Laurence Perreault Levasseur, Shirley Ho
2019	J	`jnl`	Learning Symbolic Physics with Graph Networks. CoRR Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho
2019	J	`jnl`	Learning neutrino effects in Cosmology with Convolutional Neural Networks. CoRR Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro, Siyu He, Shirley Ho, ChangHoon Hahn
2019	J	`jnl`	Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. CoRR Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho
2018	—	`conf`	Analysis of Cosmic Microwave Background with Deep Learning. ICLR (Workshop) Siyu He, Siamak Ravanbakhsh, Shirley Ho
2018	J	`jnl`	CosmoFlow: Using Deep Learning to Learn the Universe at Scale. CoRR Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Diana Moise, Simon J. Pennycook, Kristyn J. Maschhoff, Jason Sewall, Nalini Kumar, Shirley Ho, Michael F. Ringenburg, Prabhat, Victor W. Lee
2018	A	`conf`	CosmoFlow: using deep learning to learn the universe at scale. SC 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
2018	J	`jnl`	Learning to Predict the Cosmological Structure Formation. CoRR Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
2017	J	`jnl`	Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne C. Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos
2016	A*	`conf`	Estimating Cosmological Parameters from the Dark Matter Distribution. ICML Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos
2015	A	`conf`	Fast Function to Function Regression. AISTATS Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider
2015	A*	`conf`	Finding Galaxies in the Shadows of Quasars with Gaussian Processes. ICML Roman Garnett, Shirley Ho, Jeff G. Schneider
2015	—	`conf`	Optimal Ridge Detection using Coverage Risk. NIPS Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman