Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks.
Doyeong HwangSoojung YangYongchan KwonKyung-Hoon LeeGrace LeeHanseok JoSeyeol YoonSeongok RyuPublished in: J. Chem. Inf. Model. (2020)
Keyphrases
- neural network
- supervised learning
- pattern recognition
- graph theory
- unsupervised learning
- reinforcement learning
- back propagation
- learning algorithm
- structured data
- graph matching
- neural network model
- directed graph
- connected components
- random walk
- graph structure
- fuzzy logic
- graph model
- artificial neural networks
- graph databases
- training data
- graph representation
- machine learning
- fuzzy systems
- feed forward
- activation function
- fault diagnosis
- genetic algorithm
- three dimensional
- generalization error
- graph clustering
- multiple instance learning
- training set
- active learning
- graph mining
- directed acyclic graph
- semi supervised
- class labels
- multi layer
- weighted graph
- model selection
- labeled data
- bipartite graph
- neural nets
- multilayer perceptron
- unlabeled data