A normalizing flow whose latent variable combines a posterior of a predictive model for the covariate x with a Gaussian nuisance component performs conditional density estimation and supervised dimension reduction for high-dimensional responses.
Aop: An anti-overfitting pretreatment for practical image-based plant diagnosis
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data
A normalizing flow whose latent variable combines a posterior of a predictive model for the covariate x with a Gaussian nuisance component performs conditional density estimation and supervised dimension reduction for high-dimensional responses.