Science

Researchers have developed a novel framework that blends physics-based modeling with advanced machine learning to anticipate equipment failures with improved accuracy and reliability.

A new framework leverages probabilistic modeling and deep learning to reconstruct complete 3D scenes from limited depth information.

A new benchmark moves past simple accuracy to assess how well ECG foundation models actually understand heart data and generalize to real-world clinical scenarios.

A new review examines how large language models are changing the game for software maintenance and evolution, offering both powerful assistance and potential pitfalls.

Researchers have developed a novel strategy for generating reliably accurate prediction intervals for future insurance claims, even with limited data.

New research presents a powerful method for filtering unwanted signals from data organized as complex networks, improving the accuracy of analysis and prediction.

As open-source AI models proliferate, a growing concern is the collective environmental impact of their development and derivative works.

A new framework moves beyond traditional fairness metrics to assess the risks of harmful outputs from large language models, focusing on how those risks are distributed across different groups.

A new approach aligns graph structures with textual data to unlock the power of collaborative machine learning across decentralized datasets.

Researchers have developed a framework that dramatically reduces the energy demands of large language models without sacrificing performance.