A Dynamic Weighted Tabular Method for Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3231102
Abstract: Traditional Machine Learning (ML) models are generally preferred for classification tasks on tabular datasets, which often produce unsatisfactory results in complex tabular datasets. Recent works, using Convolutional Neural Networks (CNN) with embedding techniques, outperform the… read more here.
Keywords: neural networks; dynamic weighted; tabular method; convolutional neural ... See more keywords
DiffSeer: Difference-Based Dynamic Weighted Graph Visualization
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Computer Graphics and Applications"
DOI: 10.1109/mcg.2023.3248289
Abstract: Existing dynamic weighted graph visualization approaches rely on users’ mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for… read more here.
Keywords: dynamic weighted; diffseer; graph; weighted graph ... See more keywords
A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning
Sign Up to like & getrecommendations! Published in 2021 at "Mathematical Problems in Engineering"
DOI: 10.1155/2021/3625362
Abstract: Extended Kalman filter (EKF) plays an important role in the acoustic signal processing of underwater positioning. However, accumulative errors and model inaccuracies lead to divergence. Then, attenuation memory EKF is created in response to this… read more here.
Keywords: dynamic weighted; underwater positioning; weighted attenuation; attenuation memory ... See more keywords
DWFed: A statistical- heterogeneity-based dynamic weighted model aggregation algorithm for federated learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.1041553
Abstract: Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However,… read more here.
Keywords: dynamic weighted; federated learning; statistical heterogeneity; model ... See more keywords