In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...

David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to ...

Explore the Types of Machine Learning and their impact on AI. Learn how these core frameworks drive digital innovation and ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Background Hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death (SCD), and ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the ...

Federal scientists announced a new artificial intelligence tool that can forecast drought conditions 90 days ahead across the ...

Machine learning identifies HLA structural features linked to graft failure, improving prediction and donor selection in transplantation.

A new soil-moisture retrieval strategy has improved the accuracy of satellite-based moisture mapping by combining microwave reflection signals with vegetation-structure information that conventional ...

Abstract: Feature selection is a pivotal step in machine learning, aimed at reducing feature dimensionality and improving model performance. Conventional feature selection methods, typically framed as ...

Factoring out nucleotide-level mutation biases from antibody language models dramatically improves prediction of functional mutation effects while reducing computational cost by orders of magnitude.