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The Cosmic Graph: Optimal Information Extraction from Large-Scale
  Structure using Catalogues
v1v2v3 (latest)

The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues

11 July 2022
T. Lucas Makinen
Tom Charnock
Pablo Lemos
Natalia Porqueres
A. Heavens
Benjamin Dan Wandelt
ArXiv (abs)PDFHTML

Papers citing "The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues"

11 / 11 papers shown
Title
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
Natalí S. M. de Santi
F. Villaescusa-Navarro
L. Abramo
Helen Shao
Lucia A. Perez
...
F. Marinacci
D. Spergel
K. Dolag
L. Hernquist
M. Vogelsberger
249
5
0
28 Jan 2025
Learning cosmology and clustering with cosmic graphs
Learning cosmology and clustering with cosmic graphs
Pablo Villanueva-Domingo
F. Villaescusa-Navarro
AI4CE
59
37
0
28 Apr 2022
Rediscovering orbital mechanics with machine learning
Rediscovering orbital mechanics with machine learning
Pablo Lemos
N. Jeffrey
M. Cranmer
S. Ho
Peter W. Battaglia
PINNAI4CE
81
91
0
04 Feb 2022
E(n) Equivariant Normalizing Flows
E(n) Equivariant Normalizing Flows
Victor Garcia Satorras
Emiel Hoogeboom
F. Fuchs
Ingmar Posner
Max Welling
BDL
99
181
0
19 May 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
362
1,163
0
27 Apr 2021
Solving high-dimensional parameter inference: marginal posterior
  densities & Moment Networks
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
N. Jeffrey
Benjamin Dan Wandelt
70
39
0
11 Nov 2020
deep21: a Deep Learning Method for 21cm Foreground Removal
deep21: a Deep Learning Method for 21cm Foreground Removal
T. Lucas Makinen
Lachlan Lancaster
F. Villaescusa-Navarro
Peter Melchior
S. Ho
Laurence Perreault Levasseur
D. Spergel
3DPC
56
30
0
29 Oct 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases
Discovering Symbolic Models from Deep Learning with Inductive Biases
M. Cranmer
Alvaro Sanchez-Gonzalez
Peter W. Battaglia
Rui Xu
Kyle Cranmer
D. Spergel
S. Ho
AI4CE
86
483
0
19 Jun 2020
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CENAI
773
3,132
0
04 Jun 2018
Estimating Cosmological Parameters from the Dark Matter Distribution
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh
Junier Oliva
S. Fromenteau
Layne Price
S. Ho
J. Schneider
Barnabás Póczós
70
75
0
06 Nov 2017
Gaussian Error Linear Units (GELUs)
Gaussian Error Linear Units (GELUs)
Dan Hendrycks
Kevin Gimpel
180
5,056
0
27 Jun 2016
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