Abstract: We propose a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL) to compute proxy probability density functions (PDFs) that approximate the posterior PDFs of ...
Abstract: Space-time adaptive processing (STAP) is a key technique for suppressing clutter. We develop a unified correlated sparse Bayesian learning (CSBL) framework to improve clutter suppression in ...
Researchers at DeepSeek on Monday released a new experimental model called V3.2-exp, designed to have dramatically lower inference costs when used in long-context operations. DeepSeek announced the ...
Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
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Contains a wide-ranging collection of compressed sensing and feature selection algorithms. Examples include matching pursuit algorithms, forward and backward stepwise regression, sparse Bayesian ...
Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
Investigators are hoping to find clues as to why the Bayesian superyacht sank off the coast of Sicily 10 months ago, killing seven people. By Emma Bubola and Jeffrey Gettleman The hull of the Bayesian ...
Building fine-grained visual recognition systems that are capable of recognizing tens of thousands of categories, has received much attention in recent years. The well known semantic hierarchical ...
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