Learning via instructions about observations: exploring similarities and differences with learning via actual observations
Sign Up to like & getrecommendations! Published in 2022 at "Royal Society Open Science"
DOI: 10.1098/rsos.220059
Abstract: Our behaviour toward stimuli can be influenced by observing how another person (a model) interacts with those stimuli. We investigated whether mere instructions about a model's interactions with stimuli (i.e. instructions about observations) are sufficient… read more here.
Keywords: observations exploring; similarities differences; via instructions; instructions observations ... See more keywords
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3161943
Abstract: Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate on decentralized devices in modern factories. To protect… read more here.
Keywords: data heterogeneity; federated learning; heterogeneity robust; learning via ... See more keywords
Federated Learning via Attentive Margin of Semantic Feature Representations
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3209865
Abstract: Federated learning (FL) in Internet of Things (IoT) systems enables distributed model training using a large corpus of decentralized training data dispersed among multiple IoT clients. In this distributed setting, system and statistical heterogeneity, in… read more here.
Keywords: federated learning; representations learned; via attentive; margin ... See more keywords
Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3110831
Abstract: The rapid rise of IoT and Big Data has facilitated copious data-driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there… read more here.
Keywords: via removal; learning via; enhancing machine; machine learning ... See more keywords
FLAS: Computation and Communication Efficient Federated Learning via Adaptive Sampling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2021.3056655
Abstract: Federated learning enables collaborative deep learning over multiple clients without sharing their local data, and it becomes increasingly popular due to the good balance between data privacy and model usability. Generally, it faces the heavy… read more here.
Keywords: communication efficient; communication; federated learning; efficient federated ... See more keywords
Editorial: Advances in Robots Trajectories Learning via Fast Neural Networks
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2021.671519
Abstract: 1 Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Ciudad de Mexico, Mexico, Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore, Department of Mechanical and Manufacturing Engineering, University of… read more here.
Keywords: robots trajectories; learning via; via fast; editorial advances ... See more keywords
Scaling Up Q-Learning via Exploiting State–Action Equivalence
Sign Up to like & getrecommendations! Published in 2023 at "Entropy"
DOI: 10.3390/e25040584
Abstract: Recent success stories in reinforcement learning have demonstrated that leveraging structural properties of the underlying environment is key in devising viable methods capable of solving complex tasks. We study off-policy learning in discounted reinforcement learning,… read more here.
Keywords: scaling learning; structure; equivalence structure; equivalence ... See more keywords
Federated Learning via Inexact ADMM
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2204.10607
Abstract: One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient… read more here.
Keywords: via inexact; federated learning; learning via; inexact admm ... See more keywords
Supervised learning via the “hubNet” procedure
Sign Up to like & getrecommendations! Published in 2018 at "Statistica Sinica"
DOI: 10.5705/ss.202016.0482
Abstract: We propose a new method for supervised learning. The hubNet procedure fits a hub-based graphical model to the predictors, to estimate the amount of “connection” that each predictor has with other predictors. This yields a… read more here.
Keywords: learning via; procedure; model; via hubnet ... See more keywords