Shirley Ho

CoRR, 2023

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

[BibT_eX]

[DOI]

Tiberiu Tesileanu

Kyunghyun Cho

CoRR, 2023

Multiple Physics Pretraining for Physical Surrogate Models.

[BibT_eX]

[DOI]

Michael McCabe

CoRR, 2023

xVal: A Continuous Number Encoding for Large Language Models.

[BibT_eX]

[DOI]

Tiberiu Tesileanu

Kyunghyun Cho

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Learnable wavelet neural networks for cosmological inference.

[BibT_eX]

[DOI]

Christian Pedersen

Michael Eickenberg

Christian Kragh Jespersen

CoRR, 2023

Predicting the Initial Conditions of the Universe using Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2023

2022

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

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., 2022

Learning Integrable Dynamics with Action-Angle Networks.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Mangrove: Learning Galaxy Properties from Merger Trees.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

Miles D. Cranmer

Daisuke Nagai

Daniel Anglés-Alcázar

Lars Hernquist

CoRR, 2022

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

[BibT_eX]

[DOI]

Drew Jamieson

Renan Alves de Oliveira

CoRR, 2022

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

[BibT_eX]

[DOI]

Drew Jamieson

Renan Alves de Oliveira

CoRR, 2022

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

[BibT_eX]

[DOI]

Digvijay Wadekar

Leander Thiele

Daniel Anglés-Alcázar

Lars Hernquist

Drummond Buschman Fielding

CoRR, 2022

Learned Simulators for Turbulence.

[BibT_eX]

[DOI]

Kimberly L. Stachenfeld

Alvaro Sanchez-Gonzalez

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Learned Coarse Models for Efficient Turbulence Simulation.

[BibT_eX]

[DOI]

Kimberly L. Stachenfeld

Alvaro Sanchez-Gonzalez

CoRR, 2021

Super-resolving Dark Matter Halos using Generative Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

2020

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

[BibT_eX]

[DOI]

Chang Chen

Anthony Pullen

CoRR, 2020

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

[BibT_eX]

[DOI]

Renan Alves de Oliveira

CoRR, 2020

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

[BibT_eX]

[DOI]

T. Lucas Makinen

Lachlan Lancaster

Peter Melchior

Laurence Perreault Levasseur

CoRR, 2020

Anomaly Detection for Multivariate Time Series of Exotic Supernovae.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

Adrian M. Price-Whelan

Gabriella Contardo

CoRR, 2020

Lagrangian Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Discovering Symbolic Models from Deep Learning with Inductive Biases.

[BibT_eX]

[DOI]

Miles D. Cranmer

Alvaro Sanchez-Gonzalez

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.

[BibT_eX]

[DOI]

Shy Genel

CoRR, 2019

Learning neutrino effects in Cosmology with Convolutional Neural Networks.

[BibT_eX]

[DOI]

Elena Giusarma

Mauricio Reyes Hurtado

ChangHoon Hahn

CoRR, 2019

Learning Symbolic Physics with Graph Networks.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

CoRR, 2019

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks.

[BibT_eX]

[DOI]

Juan Zamudio-Fernandez

Atakan Okan

Seda Bilaloglu

Asena Derin Cengiz

Laurence Perreault Levasseur

CoRR, 2019

From Dark Matter to Galaxies with Convolutional Networks.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Learning to Predict the Cosmological Structure Formation.

[BibT_eX]

[DOI]

CoRR, 2018

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Siamak Ravanbakhsh

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

2016

Estimating Cosmological Parameters from the Dark Matter Distribution.

[BibT_eX]

[DOI]

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

2015

Optimal Ridge Detection using Coverage Risk.

[BibT_eX]

[DOI]

Yen-Chi Chen

Christopher R. Genovese

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.

[BibT_eX]

[DOI]

Roman Garnett