A General Strategy for Solving the Stochastic Point Location Problem by Utilizing the Correlation of Three Adjacent Nodes. - Ying Guo 004; Hao Ge; Jinchao Huang 001; Shenghong Li 001 (DSC, 2016)

A New Scheme Based on HSSL for Solving the Stochastic Point Location Problem. - Jinchao Huang 001; Yan Yan; Ying Guo 004; Shenghong Li 001 (DSC, 2016)

Gaussian Iteration: A Novel Way to Collaborative Filtering. - Xiaochun Li; Fangqi Li 001; Ying Guo 004; Jinchao Huang 001 (ICIC (3), 2016)

A cooperative framework of learning automata and its application in tutorial-like system. - Hao Ge; Yifan Wang 007; Shenghong Li 001; Chun-Lung Philip Chen; Ying Guo 004 (Neurocomputing, 2016)

A set of novel continuous action-set reinforcement learning automata models to optimize continuous functions. - Ying Guo 004; Hao Ge; Shenghong Li 001 (Appl. Intell., 2017)

A loss function based parameterless learning automaton scheme. - Ying Guo 004; Hao Ge; Shenghong Li 001 (Neurocomputing, 2017)

Learning automata-based algorithms for solving the stochastic shortest path routing problems in 5G wireless communication. - Ying Guo 004; Shenghong Li 001; Wen Jiang 001; Bo Zhang 063; Yinghua Ma (Phys. Commun., 2017)

Learning Automata-Based Access Class Barring Scheme for Massive Random Access in Machine-to-Machine Communications. - Chong Di 001; Bo Zhang 063; Qilian Liang; Shenghong Li 001; Ying Guo 004 (IEEE Internet Things J., 2019)

A Non-Monte-Carlo Parameter-Free Learning Automata Scheme Based on Two Categories of Statistics. - Ying Guo 004; Shenghong Li 001 (IEEE Trans. Cybern., 2019)

Learning Automata Based Competition Scheme to Train Deep Neural Networks. - Haonan Guo; Shenghong Li 001; Kaiyue Qi; Ying Guo 004; Zhengwu Xu (IEEE Trans. Emerg. Top. Comput. Intell., 2020)

A novel reduced parameter s-model of estimator learning automata in the switching non-stationary environment. - Ying Guo 004; Chong Di 001; Shenghong Li 001 (Neural Comput. Appl., 2022)

A multi-Markovian switching-based strategy for solving the stochastic point location problem. - Ying Guo 004; Shenghong Li 001 (Neural Comput. Appl., 2022)

An Auxiliary Modality Based Text-Image Matching Methodology for Fake News Detection. - Ying Guo 004; Bingxin Li; Hong Ge; Chong Di 001 (ICANN (2), 2023)

Learning automata-accelerated greedy algorithms for stochastic submodular maximization. - Chong Di 001; Fangqi Li 001; Pengyao Xu; Ying Guo 004; Chao Chen 009; Minglei Shu (Knowl. Based Syst., 2023)

BenchMFC: A benchmark dataset for trustworthy malware family classification under concept drift. - Yongkang Jiang; Gaolei Li; Shenghong Li 001; Ying Guo 004 (Comput. Secur., 2024)

Crowdsourcing Malware Family Annotation: Joint Class-Determined Tag Extraction and Weakly-Tagged Sample Inference. - Yongkang Jiang; Gaolei Li; Shenghong Li 001; Ying Guo 004; Kai Zhou 001 (IEEE Trans. Netw. Serv. Manag., 2024)

HADT: Image super-resolution restoration using Hybrid Attention-Dense Connected Transformer Networks. - Ying Guo 004; Chang Tian; Jie Liu 022; Chong Di 001; Keqing Ning (Neurocomputing, 2025)

Dmvae: a dual-stream multi-modal variational autoencoder for multi-task fake news detection. - Ying Guo 004; Shuting Hu; Yao Li; Chong Di 001; Jie Liu 022 (Pattern Anal. Appl., 2025)

HCT: image super-resolution restoration using hierarchical convolution transformer networks. - Ying Guo 004; Chang Tian; Han Wang; Jie Liu 022; Chong Di 001; Keqing Ning (Pattern Anal. Appl., 2025)

Consistency-Heterogenity Balanced Fake News Detection via Cross-Modal Matching. - Ying Guo 004; Bingxin Li; Kexin Zhen; Jie Liu 022; Gaolei Li; Qi Wang 079; Yong-Jin Liu 001 (IEEE Trans. Artif. Intell., 2025)