1 code implementation • 29 Feb 2024 • David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard
Assuming arbitrary parametric Gaussian noise, we develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the family of noise distributions, and a Monte Carlo sampler to infer the noise parameters.
no code implementations • 25 Oct 2023 • David Heurtel-Depeiges, Blakesley Burkhart, Ruben Ohana, Bruno Régaldo-Saint Blancard
We investigate diffusion-based modeling of the dust foreground and its interest for component separation.
no code implementations • 25 Oct 2023 • Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke
Simulation-based inference has been popular for amortized Bayesian computation.
1 code implementation • 4 Oct 2023 • Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana, Miles Cranmer, Alberto Bietti, Michael Eickenberg, Siavash Golkar, Geraud Krawezik, Francois Lanusse, Mariel Pettee, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
2 code implementations • 4 Oct 2023 • Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti, Miles Cranmer, Geraud Krawezik, Francois Lanusse, Michael McCabe, Ruben Ohana, Liam Parker, Bruno Régaldo-Saint Blancard, Tiberiu Tesileanu, Kyunghyun Cho, Shirley Ho
Large Language Models have not yet been broadly adapted for the analysis of scientific datasets due in part to the unique difficulties of tokenizing numbers.
1 code implementation • 26 Jun 2023 • Bruno Régaldo-Saint Blancard, Michael Eickenberg
In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations.