soilVAE: Supervised Variational Autoencoder Regression via 'reticulate'
Supervised latent-variable regression for high-dimensional predictors
such as soil reflectance spectra. The model uses an encoder-decoder neural
network with a stochastic Gaussian latent representation regularized by a
Kullback-Leibler term, and a supervised prediction head trained jointly with
the reconstruction objective (Kingma and Welling (2014)
<doi:10.48550/arXiv.1312.6114>). Predictors and response are standardised
inside the model, so new data are scaled like the training data. The
implementation interfaces R with a 'Python' deep-learning backend ('Keras'
and 'TensorFlow') and provides utilities for installation, training with
early stopping, tuning, prediction, latent embeddings, and saving models.
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