default search action
Shirley Ho
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [i42]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) - [i41]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
- [j4]Pablo Lemos
Robust simulation-based inference in cosmology with Bayesian neural networks. Mach. Learn. Sci. Technol. 4(1): 01 (2023) - [j3]Pablo Lemos
Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. 4(4): 45002 (2023) - [j2]Hanchen Wang
Scientific discovery in the age of artificial intelligence. Nat. 620(7972): 47-60 (2023) - [i40]Vaibhav Jindal, Drew Jamieson, Albert Liang, Aarti Singh, Shirley Ho
Predicting the Initial Conditions of the Universe using Deep Learning. CoRR abs/2303.13056 (2023) - [i39]Christian Pedersen, Michael Eickenberg, Shirley Ho:
Learnable wavelet neural networks for cosmological inference. CoRR abs/2307.14362 (2023) - [i38]Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR abs/2309.16645 (2023) - [i37]Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles D. Cranmer
xVal: A Continuous Number Encoding for Large Language Models. CoRR abs/2310.02989 (2023) - [i36]Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles D. Cranmer
Multiple Physics Pretraining for Physical Surrogate Models. CoRR abs/2310.02994 (2023) - [i35]François Lanusse, Liam Holden Parker, Siavash Golkar, Miles D. Cranmer
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR abs/2310.03024 (2023) - [i34]Pablo Lemos, Liam Holden Parker, ChangHoon Hahn, Shirley Ho
SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering. CoRR abs/2310.15256 (2023) - [i33]Keiya Hirashima
Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations. CoRR abs/2311.08460 (2023) - 2022
- [j1]Leander Thiele
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Mach. Learn. Sci. Technol. 3(3): 35002 (2022) - [c8]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 - [i32]Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro, J. Colin Hill, David N. Spergel, Miles D. Cranmer
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. CoRR abs/2201.01305 (2022) - [i31]Pablo Lemos
Rediscovering orbital mechanics with machine learning. CoRR abs/2202.02306 (2022) - [i30]Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks. CoRR abs/2203.00026 (2022) - [i29]Drew Jamieson, Yin Li, Siyu He, Francisco Villaescusa-Navarro, Shirley Ho
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation. CoRR abs/2206.04573 (2022) - [i28]Drew Jamieson, Yin Li, Renan Alves de Oliveira, Francisco Villaescusa-Navarro, Shirley Ho
Field Level Neural Network Emulator for Cosmological N-body Simulations. CoRR abs/2206.04594 (2022) - [i27]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) - [i26]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) - [i25]Yan-Mong Chan, Natascha Manger, Yin Li, Chao-Chin Yang, Zhaohuan Zhu, Philip J. Armitage, Shirley Ho
Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning. CoRR abs/2210.02339 (2022) - [i24]Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho
Mangrove: Learning Galaxy Properties from Merger Trees. CoRR abs/2210.13473 (2022) - [i23]Thomas Pfeil
A Neural Network Subgrid Model of the Early Stages of Planet Formation. CoRR abs/2211.04160 (2022) - [i22]Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer
Learning Integrable Dynamics with Action-Angle Networks. CoRR abs/2211.15338 (2022) - 2021
- [i21]Miles D. Cranmer
A Bayesian neural network predicts the dissolution of compact planetary systems. CoRR abs/2101.04117 (2021) - [i20]V. Ashley Villar, Miles D. Cranmer
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. CoRR abs/2103.12102 (2021) - [i19]Ji Won Park, V. Ashley Villar, Yin Li, Yan-Fei Jiang, Shirley Ho, Joshua Yao-Yu Lin, Philip J. Marshall, Aaron Roodman:
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes. CoRR abs/2106.01450 (2021) - [i18]David Schaurecker, Yin Li, Jeremy Tinker, Shirley Ho, Alexandre Refregier
Super-resolving Dark Matter Halos using Generative Deep Learning. CoRR abs/2111.06393 (2021) - [i17]Kimberly L. Stachenfeld, Drummond B. Fielding
Learned Coarse Models for Efficient Turbulence Simulation. CoRR abs/2112.15275 (2021) - 2020
- [c7]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 - [i16]Miles D. Cranmer
Lagrangian Neural Networks. CoRR abs/2003.04630 (2020) - [i15]Miles D. Cranmer
Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR abs/2006.11287 (2020) - [i14]Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer
Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. CoRR abs/2007.04459 (2020) - [i13]V. Ashley Villar, Miles D. Cranmer
Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR abs/2010.11194 (2020) - [i12]T. Lucas Makinen
deep21: a Deep Learning Method for 21cm Foreground Removal. CoRR abs/2010.15843 (2020) - [i11]Renan Alves de Oliveira
Fast and Accurate Non-Linear Predictions of Universes with Deep Learning. CoRR abs/2012.00240 (2020) - [i10]Chang Chen, Yin Li, Francisco Villaescusa-Navarro
Learning the Evolution of the Universe in N-body Simulations. CoRR abs/2012.05472 (2020)
2010 – 2019
- 2019
- [i9]Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Siyu He, Gabriella Contardo, Francisco Villaescusa-Navarro
From Dark Matter to Galaxies with Convolutional Networks. CoRR abs/1902.05965 (2019) - [i8]Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks. CoRR abs/1904.12846 (2019) - [i7]Miles D. Cranmer
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. CoRR abs/1908.08045 (2019) - [i6]Miles D. Cranmer
Learning Symbolic Physics with Graph Networks. CoRR abs/1909.05862 (2019) - [i5]Elena Giusarma
Learning neutrino effects in Cosmology with Convolutional Neural Networks. CoRR abs/1910.04255 (2019) - [i4]Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang, Wei Zhang, Yueqiu Sun, Gabriella Contardo, Francisco Villaescusa-Navarro
From Dark Matter to Galaxies with Convolutional Neural Networks. CoRR abs/1910.07813 (2019) - 2018
- [c6]Siyu He, Siamak Ravanbakhsh, Shirley Ho:
Analysis of Cosmic Microwave Background with Deep Learning. ICLR (Workshop) 2018 - [c5]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:
CosmoFlow: using deep learning to learn the universe at scale. SC 2018: 65:1-65:11 - [i3]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:
CosmoFlow: Using Deep Learning to Learn the Universe at Scale. CoRR abs/1808.04728 (2018) - [i2]Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos:
Learning to Predict the Cosmological Structure Formation. CoRR abs/1811.06533 (2018) - 2017
- [i1]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau
Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR abs/1711.02033 (2017) - 2016
- [c4]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. ICML 2016: 2407-2416 - 2015
- [c3]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - [c2]Roman Garnett, Shirley Ho, Jeff G. Schneider:
Finding Galaxies in the Shadows of Quasars with Gaussian Processes. ICML 2015: 1025-1033 - [c1]Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman:
Optimal Ridge Detection using Coverage Risk. NIPS 2015: 316-324
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 2024-10-09 20:27 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: