A new “periodic table for machine learning,” is reshaping how researchers explore AI, unlocking fresh pathways for discovery. The framework, Information-Contrastive Learning (I-Con), connects diverse ...

discuss some of the most common machine learning algorithms, and explain how those algorithms relate to the other pieces of the puzzle of creating predictive models from historical data.

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

The full dataset contained 2,523 compounds and included compounds with both senolytic and non-senolytic properties so as not to bias the machine-learning algorithm. The algorithm was then used to ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation ...

Researchers at the University of Massachusetts Amherst's Institute for Applied Life Sciences (IALS) and Embr Labs have created a machine-learning algorithm to predict a hot flash before a person ...

Machine learning algorithm predicts how to get the most out of electric vehicle batteries. ScienceDaily . Retrieved May 23, 2025 from www.sciencedaily.com / releases / 2022 / 08 / 220823162725.htm ...

Devoted to faculty and students that are interested in developing new machine learning algorithms and techniques, and seek to deepen our understanding of existing ones. Machine learning provides the ...

An automated machine-learning program developed by researchers ... bone density scans taken during routine clinical testing. The algorithm shortens the timeframe to screen for AAC significantly ...