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 ...

Predicting the adhesive force between steel reinforcement and concrete is crucial as it influences stress distribution and the overall mechanical behavior of reinforced concrete. This study proposes a ...

MESU is a Bayesian framework that balances learning and forgetting by leveraging synaptic uncertainty, enabling continual learning without task boundaries while mitigating catastrophic forgetting, and ...

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 ...

Concept Bottleneck Models (CBMs) have been proposed as a compromise between white-box and black-box models, aiming to achieve interpretability without sacrificing accuracy. The standard training ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...

Abstract: Concept drift in streaming data poses a significant challenge to the stability and performance of deep learning models. This study explores an uncertainty-based drift detection approach ...

Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint proteins known as transcriptional regulators that control how genes turn on ...