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 ...

Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Recent survey delivers the first systematic benchmark of TSP solvers spanning end-to-end deep learners, hybrid methods and ...

This course covers three major algorithmic topics in machine learning. Half of the course is devoted to reinforcement learning with the focus on the policy gradient and deep Q-network algorithms. The ...

WEST LAFAYETTE, Ind. — To expand the availability of electricity generated from nuclear power, several countries have started developing designs for small modular reactors (SMRs), which could take ...

Researchers have successfully demonstrated quantum speedup in kernel-based machine learning. When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.

In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...

The study addresses heterogeneous UAV cooperative task assignment under complex constraints via an energy learning ...

Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price. Unhappy with their meager profits, they meet one night in a ...