Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial ...

Genomic surveillance—the process of monitoring and sequencing pathogens—is one of the most important tools for detecting ...

LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.

Abstract: A number of machine learning (ML) algorithm based small signal modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) have been reported in literature. However, these ...

Background: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall detection, in-hospital falls, or conventional ...

Reference: Perego S. & Bonati L. Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling, npj Computational Materials 10, 291 (2024) doi: ...

ABSTRACT: In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning ...

Abstract: Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence ...