A new communication-collective system, OptiReduce, speeds up AI and machine learning training across multiple cloud servers by setting time boundaries rather than waiting for every server to catch up, ...

In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives ...

An automated machine-learning program developed by researchers from Edith Cowan University (ECU) in conjunction with the ...

Researchers at Rice University have developed a new machine learning (ML) algorithm that excels at interpreting the "light ...

Sticking to an exercise routine is a challenge many people face. But a research team is using machine learning to uncover what keeps individuals committed to their workouts.

Department of EECS, University of California at Berkeley, 485 Soda Hall, Berkeley CA 94720-1776, USA Previously at: Institute for Adaptive and Neural Computation, University of Edinburgh, UK.

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 ...

In 2017, Ezekiel Emanuel, a well-known oncologist and health policy commentator, said radiologists would soon be out of work thanks to machine learning. That hasn’t happened, but although ...

A machine-learning algorithm, catGRANULE 2.0 ROBOT, has been developed to predict the potential of proteins to form toxic aggregates linked to neurodegenerative diseases like ALS, Parkinson's, and ...

We propose a distributed QoE-oriented computation offloading (QECO) algorithm based on deep reinforcement learning (DRL) that empowers mobile devices to make their offloading decisions without ...

Backed by companies like Nvidia Corp. NVDA, Amazon.com Inc. AMZN, and Meta Platforms Inc. META, Scale AI is reportedly seeking a valuation of up to $25 billion in a new tender offer. What Happened ...