CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

7 June 2017

Papers citing "CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks"

8 / 8 papers shown

Title
Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space Tigran Ramazyan M. Hushchyn D. Derkach 34 0 0 16 Jul 2024
Can denoising diffusion probabilistic models generate realistic astrophysical fields? N. Mudur D. Finkbeiner DiffM 21 15 0 22 Nov 2022
Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy Michael J. Smith James E. Geach 35 32 0 07 Nov 2022
Machine Learning in the Search for New Fundamental Physics G. Karagiorgi Gregor Kasieczka S. Kravitz Benjamin Nachman David Shih AI4CE 42 113 0 07 Dec 2021
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas TDI FAtt 32 17 0 26 Nov 2021
Generalization Error of GAN from the Discriminator's Perspective Hongkang Yang Weinan E GAN 46 13 0 08 Jul 2021
Painting with baryons: augmenting N-body simulations with gas using deep generative models T. Tröster C. Ferguson J. Harnois-Déraps I. McCarthy AI4CE 21 49 0 28 Mar 2019
Fast cosmic web simulations with generative adversarial networks Andrés C. Rodríguez T. Kacprzak Aurelien Lucchi A. Amara R. Sgier J. Fluri Thomas Hofmann Alexandre Réfrégier GAN AI4CE 21 89 0 27 Jan 2018