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Found 5 papers, 2 papers with code
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
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.
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models.
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms.
Realistic galaxy image simulation via score-based generative models
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
Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model
In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system.
