Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining.
Tim KalerNickolas StathasAnne OuyangAlexandros-Stavros IliopoulosTao B. SchardlCharles E. LeisersonJie ChenPublished in: CoRR (2021)
Keyphrases
- neural network
- training process
- training algorithm
- structured prediction
- error back propagation
- multi layer perceptron
- graph representation
- feedforward neural networks
- feed forward neural networks
- pattern recognition
- graph theory
- random walk
- back propagation
- gibbs sampler
- backpropagation algorithm
- neural network training
- supervised learning
- multi layer
- clique tree
- probabilistic inference
- training set
- directed graph
- neural network structure
- markov chain monte carlo
- radial basis function network
- graph structure
- random sampling
- weighted graph
- bayesian inference
- bipartite graph
- belief networks
- multilayer perceptron
- neural network model
- monte carlo
- test set
- fuzzy logic
- decision trees
- graph model
- feed forward
- training examples
- bayesian networks