Title
Bayesian compression for dynamically expandable networks
Abstract
•A compact model structure with preserving the accuracy via sparsity inducing priors, which leads to fewer neurons at each hidden layer in the network, equivalently fewer parameters.•Dynamically expands network capacity with only the necessary number of neurons by employing sparsity inducing priors for the added neurons, so as to increase the network capacity when necessary.•Variational Bayesian approximation for the model parameters with parameter uncertainty.
Year
DOI
Venue
2022
10.1016/j.patcog.2021.108260
Pattern Recognition
Keywords
DocType
Volume
Bayesian compression,DEN,Continual learning,Selective retraining,Dynamically expands network,Semantic drift
Journal
122
Issue
ISSN
Citations 
1
0031-3203
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yang Yang112328.79
Bo Chen230434.22
Hongwei Liu321719.48