mlsanityData to 14 Oct 2026

Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM

arXiv 2303.07487 ↗First posted 13 Mar 2023stat.ML cs.LG q-bio.QMcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 13 Mar 2023.

Builds on

14 of 31 references.

References · 14

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2 more
  1. 2017Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensionscited by 4
  2. 2019Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EMcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstructioncited by 0