Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM
Machine Learning
2023-05-12 v2 Machine Learning
Quantitative Methods
Abstract
Variational autoencoders (VAEs) are a popular generative model used to approximate distributions. The encoder part of the VAE is used in amortized learning of latent variables, producing a latent representation for data samples. Recently, VAEs have been used to characterize physical and biological systems. In this case study, we qualitatively examine the amortization properties of a VAE used in biological applications. We find that in this application the encoder bears a qualitative resemblance to more traditional explicit representation of latent variables.
Keywords
Cite
@article{arxiv.2303.07487,
title = {Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM},
author = {Daniel G. Edelberg and Roy R. Lederman},
journal= {arXiv preprint arXiv:2303.07487},
year = {2023}
}