EurekAlert!

Energy learning hyper-heuristic algorithm for cooperative task assignment of heterogeneous UAVs under complex constraints

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

Evolutionary Multi-Objective Deep Reinforcement Learning for Task Offloading in Industrial Internet of Things

Abstract: Mobile Edge Computing (MEC) plays a pivotal role in optimizing the Industrial Internet of Things (IIoT), where the Industrial Task Offloading Problem (ITOP) is crucial for ensuring optimal ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...
Eos

Deep Learning Goes Multi-Tasking

Deep learning’s (DL’s) promise and appeal is algorithmic amalgamation of all available data to achieve model generalization and prediction of complex systems. Thus, there is a need to design ...
C&EN

Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. A few public databases provide biological activity data for ...
The New York Times

Parents, Your Job Has Changed in the A.I. Era

Ms. Anderson and Ms. Winthrop are the authors of “The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better.” As the new school year gets underway, artificial intelligence is ...
Scientific Research Publishing

Ruder, S. (2017) An Overview of Multi-Task Learning in Deep Neural Networks. arXiv: 1706.05098.

ABSTRACT: Accurately predicting medication response and disease severity is essential for advancing personalized treatment strategies, especially in complex neuropsychiatric conditions. In this study, ...
Scientific Research Publishing

Vithayathil Varghese, N. and Mahmoud, Q.H. (2020) A Survey of Multi-Task Deep Reinforcement Learning. Electronics, 9, Article 1363.

ABSTRACT: Accurately predicting medication response and disease severity is essential for advancing personalized treatment strategies, especially in complex neuropsychiatric conditions. In this study, ...
Frontiers

Seismic first break picking based on multi-task learning

Introduction: Seismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot ...
IEEE

Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

Abstract: Split computing has emerged as a promising approach to alleviate the resource constraints of IoT devices by offloading computation to edge servers. However, conventional split computing ...
Frontiers

Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer’s disease

Introduction: Correlated phenotypes may have both shared and unique causal factors, and jointly modeling these phenotypes can enhance prediction performance by enabling efficient information transfer.