On the magnitude of parameters of RBMs being universal approximators. - Linyan Gu; Lihua Yang (ICPR, 2016)

On the Representational Power of Restricted Boltzmann Machines for Symmetric Functions and Boolean Functions. - Linyan Gu; Jianfeng Huang 001; Lihua Yang (IEEE Trans. Neural Networks Learn. Syst., 2019)

Towards the representational power of restricted Boltzmann machines. - Linyan Gu; Feng Zhou; Lihua Yang (Neurocomputing, 2020)

Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images. - Linyan Gu; Xiao-Chuan Cai (Artif. Intell. Medicine, 2021)

IF2CNN: Towards non-stationary time series feature extraction by integrating iterative filtering and convolutional neural networks. - Feng Zhou 018; Haomin Zhou 001; Zhihua Yang; Linyan Gu (Expert Syst. Appl., 2021)

A novel image deblocking approach within a graph framework. - Feng Zhou; Huafei Li; Lihua Yang; Zhihua Yang; Linyan Gu (Digit. Signal Process., 2022)

Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks. - Linyan Gu; Lihua Yang; Feng Zhou 018 (Neural Networks, 2022)

Refinements of Approximation Results of Conditional Restricted Boltzmann Machines. - Linyan Gu; Lihua Yang; Feng Zhou 018 (IEEE Trans. Neural Networks Learn. Syst., 2023)

A novel graph-based feature interaction model for click-through rate prediction. - Qianlong He; Feng Zhou; Linyan Gu; Zhibin Yuan (Inf. Sci., 2023)

A parallel domain decomposition-based implicit finite volume lattice Boltzmann method for incompressible thermal convection flows on unstructured grids. - Lei Xu 018; Rongliang Chen; Linyan Gu; Wu Zhang (J. Comput. Appl. Math., 2025)