no code implementations • 24 Jul 2024 • Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur
In these cases, corrupted data or a surrogate, e. g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors.
no code implementations • 6 Feb 2024 • Pablo Lemos, Sammy Sharief, Nikolay Malkin, Salma Salhi, Connor Stone, Laurence Perreault-Levasseur, Yashar Hezaveh
We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models.
no code implementations • 29 Nov 2023 • Alexandre Adam, Connor Stone, Connor Bottrell, Ronan Legin, Yashar Hezaveh, Laurence Perreault-Levasseur
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms.
2 code implementations • 2 Nov 2021 • Michael J. Smith, James E. Geach, Ryan A. Jackson, Nikhil Arora, Connor Stone, Stéphane Courteau
We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies.
Ranked #1 on
Galaxy emergent property recreation
on SDSS Galaxies
1 code implementation • 1 Oct 2020 • Michael J. Smith, Nikhil Arora, Connor Stone, Stéphane Courteau, James E. Geach
In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system.