Probabilistic neural network framework embeds linear equality constraints for dynamic chemical process modeling, showing improved accuracy, calibration, and constraint adherence on reduced data plus faster training on large data.
A Survey of Uncertainty in Deep Neural Networks, January 2022
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2verdicts
UNVERDICTED 2representative citing papers
Framework embeds aleatoric and epistemic uncertainties into BNN parameter variances and applies moment propagation for sampling-free variational inference in lightweight networks.
citing papers explorer
-
Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Probabilistic neural network framework embeds linear equality constraints for dynamic chemical process modeling, showing improved accuracy, calibration, and constraint adherence on reduced data plus faster training on large data.
-
A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties
Framework embeds aleatoric and epistemic uncertainties into BNN parameter variances and applies moment propagation for sampling-free variational inference in lightweight networks.