LAUSR

About LAUSR
Donate
Contact Us

Follow us:

You are not signed in:

Sign Up!


   Articles with "learning via" as a keyword



Learning via instructions about observations: exploring similarities and differences with learning via actual observations

Sign Up to like & get
recommendations! 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

Multi-Agent Q-Learning via Best Choice Dynamics

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3612969

Abstract: Motivated by multi-agent Q-learning scenarios, this paper introduces a distributed action selection algorithm that relies on individual agents interacting with local neighbors to learn a joint action. The algorithm, termed Best Choice Dynamics, has each… read more here.

Keywords: agent learning; learning via; best choice; multi agent ... See more keywords

Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT

Sign Up to like & get
recommendations! 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 & get
recommendations! 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

Heterogeneous Federated Learning via Generative Model-Aided Knowledge Distillation in the Edge

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2024.3488565

Abstract: Federated learning (FL) has been popular recently as a framework for training machine learning (ML) models in a distributed and privacy-preserving manner. Traditional FL frameworks often struggle with model and statistical heterogeneity among participating clients,… read more here.

Keywords: learning via; knowledge; distillation; federated learning ... See more keywords

Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies

Sign Up to like & get
recommendations! 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 & get
recommendations! 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

Contrastive Learning via Variational Information Bottleneck

Sign Up to like & get
recommendations! Published in 2025 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2025.3571990

Abstract: Recent advances in self-supervised learning have witnessed great achievements, especially with the introduction of contrastive learning, where the goal is to maximize the mutual information between different augmentations of the same image, i.e., positive pairs.… read more here.

Keywords: contrastive learning; information; learning via; information bottleneck ... See more keywords

Robust Semi-Decentralized Federated Learning via Collaborative Relaying

Sign Up to like & get
recommendations! Published in 2024 at "IEEE Transactions on Wireless Communications"

DOI: 10.1109/twc.2023.3342095

Abstract: Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks. The lack of constant connectivity induces a large generalization gap, especially when the local data distribution amongst… read more here.

Keywords: robust semi; learning via; semi decentralized; collaborative relaying ... See more keywords

Editorial: Advances in Robots Trajectories Learning via Fast Neural Networks

Sign Up to like & get
recommendations! 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 & get
recommendations! 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