PyTorch implementation of graph convolutional networks (GCNs).
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Updated
Sep 28, 2019 - Python
PyTorch implementation of graph convolutional networks (GCNs).
Linkage-based multi-object clustering/grouping using GCN
A novel method for link prediction in temporal networks based on EvolveGCN (Aldo Pareja et al) and GAT (Petar Velickovic et al)
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
Calculating the nearest weather sensor for each traffic sensor and then merging the weather sensors' temporal data with the traffic sensors'.
Code for my Master's thesis "Exploiting Spatial-Temporal Relationships for Occlusion-Robust 3D Human Pose Estimation" at TUM
a novel transformer-based architecture named CSTTN for traffic prediction
Implementation of various collaborative filtering methods for recommender systems with implicit feedback
Modeling the external convergence from photometric catalogs
Survival Prediction for Gastric Cancer via Multimodal Learning of Whole Slide Images and Gene Expression -- BIBM 2022
Predicting probable drug-binding sites for thousands of human proteins using AlphaFold2 predicted 3D protein structures.
Fraud detection using Graph Convolutional Networks
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
Small Molecular Graph Generation for Drug Discovery
The implementation of paper "HPOFiller: identifying missing protein-phenotype associations by graph convolutional network".
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.
NLP - Semantic Role Labeling using GCN, Bert and Biaffine Attention Layer. Developed in Pytorch
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