Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls.
Stephan ThalerGregor DoehnerJulija ZavadlavPublished in: CoRR (2022)
Keyphrases
- neural network
- decision theory
- dempster shafer
- artificial neural networks
- back propagation
- inherent uncertainty
- learning vector quantization
- maximum likelihood
- bp neural network
- neural network is trained
- web scale
- highly scalable
- potential functions
- bayesian networks
- pattern recognition
- higher order
- recurrent neural networks
- uncertain data
- bayesian inference
- network model
- posterior distribution
- neural network model
- high order
- genetic algorithm
- multilayer perceptron
- fuzzy artmap
- fault diagnosis
- utility function
- bayesian learning
- bayesian methods
- training algorithm
- neural nets
- maximum entropy
- prediction model