Approximation- and Quantization-Aware Training for Graph Neural Networks.
Rodion NovkinFlorian KlemmeHussam AmrouchPublished in: IEEE Trans. Computers (2024)
Keyphrases
- neural network
- training process
- training algorithm
- feed forward neural networks
- error back propagation
- backpropagation algorithm
- multi layer perceptron
- back propagation
- pattern recognition
- feedforward neural networks
- random walk
- fuzzy logic
- artificial neural networks
- structured data
- self organizing maps
- closed form
- directed graph
- graph structure
- radial basis function network
- neural nets
- approximation error
- graph model
- neural network training
- neural network structure
- multi layer
- weighted graph
- graph theory
- multilayer perceptron
- test set
- training samples
- supervised learning
- bipartite graph
- graph partitioning
- feed forward
- graph representation
- connected components
- special case
- training set
- support vector
- genetic algorithm