This article designs a new hierarchical distributed data-driven adaptive learning control algorithm to accomplish the leader-following tracking control objective for nonaffine nonlinear multiagent ...

Alana Heath shares how mass experiential learning empowers students with real-world skills, scaling impact without sacrificing academic rigor on DisruptEd.

What is supervised learning and how does it work? In this video/post, we break down supervised learning with a simple, real-world example to help you understand this key concept in machine learning.

Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients ...

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related to peptide sequences and their resulting physicochemical ...

In this article, a model-free reinforcement learning (RL) approach is proposed for solving the optimal consensus control issue of nonlinear discrete-time multiagent systems with input constraint. To ...

The Department of Elementary and Secondary Education has begun a multi-year strategy to integrate AI into K-12 schools.

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, ...

News Release 10-Mar-2025 Deep reinforcement learning optimizes distributed manufacturing scheduling Peer-Reviewed Publication Higher Education Press image: Multi-agent training framework.