To tackle this limitation, we propose an autoencoder framework. The encoder produces an intermediate representation from the observed variables, and the decoder is a generative latent variable model ...

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Principal Components Analysis is done on the (#images, latent_space) sized matrix which contains all 70000 encoded images as 512 sized vectors.

The model is trained until the loss is minimized and the data is reproduced as closely as possible. Through this process, an autoencoder can learn the important features of the data. While that’s a ...

This naturally suggests the selection of canonical variables, in the spirit of principal components, to enable matching/calibration among different observation modalities/instruments. We develop a ...

Conceptual overview about Variational Autoencoder Modular Bayesian Network VAMBN) approach: In a first step, a low dimensional representation of known modules of variables is learned via HI-VAEs. The ...