ESS-ReduNet speeds ReduNet training by dynamically boosting the expansion operator and correcting membership estimates with label-derived Bayesian posteriors, reporting more than 10x faster convergence on several datasets.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
ESS-ReduNet: Enhancing Subspace Separability of ReduNet via Dynamic Expansion with Bayesian Inference
ESS-ReduNet speeds ReduNet training by dynamically boosting the expansion operator and correcting membership estimates with label-derived Bayesian posteriors, reporting more than 10x faster convergence on several datasets.