New research demonstrates that Bayesian Neural Networks can be dramatically compressed for efficient deployment without sacrificing their crucial ability to estimate prediction uncertainty.
Researchers have developed a new framework to automatically design efficient neural networks that can deliver faster performance on resource-constrained devices.
A new approach to time series forecasting prioritizes managing risk and uncertainty, crucial for applications like clinical decision support where errors can have serious consequences.
This review connects the ability to forecast and diagnose faults in complex systems to a fundamental property called ‘pre-normality’, offering new strategies for design and control.
New research shows how to prevent large language models from losing their safety guardrails as they are continuously updated with new information.
A novel Bayesian framework leverages probabilistic modeling to discover underlying equations from data, even in the presence of significant noise.
As healthcare increasingly relies on IoT and cloud computing, ensuring data privacy and security is paramount.
Researchers have developed a novel model that combines the strengths of wavelet transforms and transformer networks to achieve more accurate and efficient time series predictions.
New research demonstrates how connecting leading artificial intelligence models to structured knowledge dramatically improves their accuracy and reasoning skills.
A new deep learning approach offers a faster, non-invasive way to assess coronary artery blood pressure using standard imaging techniques.