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Found 5 papers, 3 papers with code
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
For each test set of 1, 000 sightlines, the BGNN infers the individual $\kappa$ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population.
Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased.
Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
We show that the posterior PDFs are sufficiently accurate (i. e., statistically consistent with the truth) across a wide variety of power-law elliptical lens mass distributions.
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis
We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation.
Simulated Annealing for JPEG Quantization
JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG.
