The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data. (arXiv:2110.06672v2 [cs.LG] UPDATED)

Learning low-dimensional representations of single-cell transcriptomics has
become instrumental to its downstream analysis. The state of the art is
currently represented by neural network models such as Variational Autoencoders
(VAEs) which use a variational approximation of the likelihood for inference.
We here present the Deep Generative Decoder (DGD), a simple generative model
that computes model parameters and representations directly via maximum a
posteriori (MAP) estimation. The DGD handles complex parametrized latent
distributions naturally unlike VAEs which typically use overly simple …

arxiv data decoder map modeling rna