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Found 6 papers, 4 papers with code
Listening to the Noise: Blind Denoising with Gibbs Diffusion
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.
Simulation-based stacking
Simulation-based inference has been popular for amortized Bayesian computation.
Removing Dust from CMB Observations with Diffusion Models
We investigate diffusion-based modeling of the dust foreground and its interest for component separation.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling.
xVal: A Continuous Number Encoding for Large Language Models
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.
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
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.
