TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks.
Hazem Peter SamoaaAntonio LongaMazen MohamadMorteza Haghir ChehreghaniPhilipp LeitnerPublished in: CoRR (2022)
Keyphrases
- neural network
- protein function prediction
- prediction model
- prediction accuracy
- neural network ensemble
- prediction algorithm
- graph representation
- pattern recognition
- artificial neural networks
- random walk
- graph theory
- artificial neural networks to predict
- genetic algorithm
- high quality
- graph based algorithm
- highly accurate
- multi layer perceptron
- spanning tree
- fuzzy logic
- high accuracy
- radial basis function network
- graph partitioning
- directed graph
- graph model
- prediction error
- weighted graph
- graph structure
- training process
- back propagation
- graph theoretic
- radial basis function neural network
- structured data
- neural nets
- fault diagnosis
- recurrent neural networks
- feed forward
- chaotic time series
- short term prediction
- neural network model