Pinned Repositories
FNAE
This is the supplementary file for the paper entitled “A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data”. Additional tables and figures are put into this file and cited by this paper.
GLCPN
This is the supplementary file for the paper entitled “Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data”. The proofs, additional tables and figures are put into this file and cited by the paper.
ICDM-2022-TLGCN
This is the PyTorch implementation for the paper entitled "A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation", which has been acceppted by ICDM2022.
TGLFA
This is the supplementary file and PyTorch implementation for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”.
TSC-2022-TGLFA
This is the PyTorch implementation for the paper entitled "Two-Stream Graph Convolutional Network- Incorporated Latent Feature Analysis".
TSC-2022-TGLFA-Supplementary-File
This is the supplementary file for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”. Additional tables and figures are put into this file and cited by the paper.
Oak-B's Repositories
Oak-B/ICDM-2022-TLGCN
This is the PyTorch implementation for the paper entitled "A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation", which has been acceppted by ICDM2022.
Oak-B/FNAE
This is the supplementary file for the paper entitled “A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data”. Additional tables and figures are put into this file and cited by this paper.
Oak-B/GLCPN
This is the supplementary file for the paper entitled “Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data”. The proofs, additional tables and figures are put into this file and cited by the paper.
Oak-B/TGLFA
This is the supplementary file and PyTorch implementation for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”.
Oak-B/TSC-2022-TGLFA
This is the PyTorch implementation for the paper entitled "Two-Stream Graph Convolutional Network- Incorporated Latent Feature Analysis".
Oak-B/TSC-2022-TGLFA-Supplementary-File
This is the supplementary file for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”. Additional tables and figures are put into this file and cited by the paper.