As a fundamental technology of artificial intelligence, existing machine learning (ML) methods often rely on extensive human intervention and manually presetting, like manually collecting, selecting, ...

Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...

Active learning represents a transformative paradigm in machine learning, aimed at reducing the annotation burden by selectively querying the most informative data points. This approach leverages ...

According to the U.S. Bureau of Labor Statistics, there are more than 10.1 million unfilled jobs, with just 5.5 million job seekers on the hunt, as of writing this article. This means there are more ...

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a ...

Self-supervised models generate implicit labels from unstructured data rather than relying on labeled datasets for supervisory signals. Self-supervised learning (SSL), a transformative subset of ...

Mingai Li, received her B.Sc. degree and M.Sc. degree from Daqing Petroleum Institute, Heilongjiang, China, in 1987 and 1990 respectively, and Ph.D. degree from Beijing University of Technology, ...

Unsupervised machine learning explores data to find new patterns without set goals. It fuels advancements in tech fields like autonomous driving and content recommendations. Investors can use ...

Researchers have used machine learning and supercomputer simulations to investigate how tiny gold nanoparticles bind to blood proteins. The studies discovered that favorable nanoparticle-protein ...

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring irradiance sensors during operation, using routine meteorological data instead.