Ye Yuan

Orcid: 0000-0002-1274-2285

Affiliations:
  • Chinese Academy of Sciences, Institute of Green and Intelligent Technology, Chongqing, China


According to our database1, Ye Yuan authored at least 36 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data.
IEEE CAA J. Autom. Sinica, November, 2024

A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis.
IEEE Trans. Fuzzy Syst., July, 2024

A Nonlinear PID-Incorporated Adaptive Stochastic Gradient Descent Algorithm for Latent Factor Analysis.
IEEE Trans Autom. Sci. Eng., July, 2024

SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks.
IEEE CAA J. Autom. Sinica, July, 2024

Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis.
IEEE Trans. Syst. Man Cybern. Syst., April, 2024

Adaptive Divergence-Based Non-Negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices From Industrial Applications.
IEEE Trans. Emerg. Top. Comput. Intell., April, 2024

An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis.
CoRR, 2024

2023
An Adaptive Divergence-Based Non-Negative Latent Factor Model.
IEEE Trans. Syst. Man Cybern. Syst., October, 2023

A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data.
IEEE Trans. Cybern., September, 2023

A Neighbor-Induced Graph Convolution Network for Undirected Weighted Network Representation.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

An Adaptive PID-Incorporated Non-Negative Latent Factor Analysis Model.
Proceedings of the IEEE International Conference on Data Mining, 2023

2022
Latent Factor Analysis for High-dimensional and Sparse Matrices - A particle swarm optimization-based approach
Springer Briefs in Computer Science, Springer, ISBN: 978-981-19-6702-3, 2022

Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis.
IEEE Trans. Knowl. Data Eng., 2022

An α-β-Divergence-Generalized Recommender for Highly Accurate Predictions of Missing User Preferences.
IEEE Trans. Cybern., 2022

A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices.
IEEE Trans. Big Data, 2022

A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs.
CoRR, 2022

PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis.
CoRR, 2022

Adaptive Divergence-based Non-negative Latent Factor Analysis.
CoRR, 2022

Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks.
Proceedings of the IEEE International Conference on Networking, Sensing and Control, 2022

A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model.
Proceedings of the IEEE International Conference on Networking, Sensing and Control, 2022

Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization.
Proceedings of the IEEE International Conference on Networking, Sensing and Control, 2022

2021
Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems.
IEEE Trans. Syst. Man Cybern. Syst., 2021

A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model.
Neurocomputing, 2021

Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems.
Neurocomputing, 2021

Dynamic Community Detection via Kalman Filter-Incorporated Non-negative Matrix Factorization.
Proceedings of the IEEE International Conference on Networking, Sensing and Control, 2021

2020
A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems.
Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

A Nonlinear Proportional Integral Derivative-Incorporated Stochastic Gradient Descent-based Latent Factor Model.
Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

Accelerated Latent Factor Analysis for Recommender Systems via PID Controller.
Proceedings of the IEEE International Conference on Networking, Sensing and Control, 2020

Adaptive Regularization-Incorporated Latent Factor Analysis.
Proceedings of the 2020 IEEE International Conference on Knowledge Graph, 2020

Temporal Web Service QoS Prediction via Kalman Filter-Incorporated Latent Factor Analysis.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications.
IEEE CAA J. Autom. Sinica, 2019

2018
A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications.
IEEE Trans. Ind. Informatics, 2018

Effects of preprocessing and training biases in latent factor models for recommender systems.
Neurocomputing, 2018

Performance of nonnegative latent factor models with β-distance functions in recommender systems.
Proceedings of the 15th IEEE International Conference on Networking, Sensing and Control, 2018

2017
Performance of latent factor models with extended linear biases.
Knowl. Based Syst., 2017

Effect of linear biases in latent factor models on high-dimensional and sparse matrices from recommender systems.
Proceedings of the 14th IEEE International Conference on Networking, Sensing and Control, 2017


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