Graph Convolutional Networks
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Updated
Aug 9, 2019 - Python
Graph Convolutional Networks
PyTorch implementation of graph convolutional networks (GCNs).
STAD-GCN: Spatial-Temporal Attention-based Dynamic Graph Convolutional Network for Retail Market Price Prediction, pytorch version (ESWA 2024)
The implementation, training and evaluation of a Structure Seer machine learning model designed for reconstruction of adjacency of a molecular graph from the labelling of its nodes.
Code for my Master's thesis "Exploiting Spatial-Temporal Relationships for Occlusion-Robust 3D Human Pose Estimation" at TUM
Calculating the nearest weather sensor for each traffic sensor and then merging the weather sensors' temporal data with the traffic sensors'.
a novel transformer-based architecture named CSTTN for traffic prediction
A machine learning model that builds amino acids into a protein model.
Modeling the external convergence from photometric catalogs
HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations
An Optimized Spatiotemporal Encoder-Decoder Neural Network for Ride-Sourcing Demand Prediction
Correlation Graph Convolutional Networks on Gene Expression and other Biology data
A Tensorflow implementation of a higher order graph convolutional neural network
The implementation of paper "HPOFiller: identifying missing protein-phenotype associations by graph convolutional network".
NLP - Semantic Role Labeling using GCN, Bert and Biaffine Attention Layer. Developed in Pytorch
Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks.
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