Bayesian thinking helps you make better decisions by updating your beliefs when new evidence appears. Even in games of chance like scratch-off lotteries, paying attention to information can improve ...

Abstract: Sparse Bayesian learning (SBL) is an advanced statistical framework that dominantly enhances the sparse features of targets of interest in radar imagery. A widely adopted strategy for ...

Abstract: Variational Bayesian learning (VBL)-aided extended target localization is conceived for orthogonal frequency division multiplexing (OFDM) based-mmWave MIMO systems using the OFDM integrated ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

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 ...

We explain the ways students haven’t recovered. By David Leonhardt Schoolchildren in Massachusetts, Ohio and Pennsylvania are still about half a year behind typical pre-Covid reading levels. In ...

Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved state-of-the-art results in this area, they require large amounts of labeled ...

Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical ...