A generative model for dimensionality reduction with millions of features and few samples
In this paper, we demonstrate that it is feasible to train a deep generative model for dimensionality reduction with millions of features using relatively few samples, making this type of generative model a more versatile alternative to standard dimensionality reduction methods. Specifically, we hypothesize that, for a decoder-only model, the number of training samples required is almost…
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