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A Tutorial on the Mathematical Model of Single Cell Variational Inference

arXiv 2101.00650 ↗First posted 3 Jan 2021q-bio.OT cs.LG q-bio.GNcited by 0

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Abstract (arXiv)

As the large amount of sequencing data accumulated in past decades and it is still accumulating, we need to handle the more and more sequencing data. As the fast development of the computing technologies, we now can handle a large amount of data by a reasonable of time using the neural network based model. This tutorial will introduce the the mathematical model of the single cell variational inference (scVI), which use the variational auto-encoder (building on the neural networks) to learn the distribution of the data to gain insights. It was written for beginners in the simple and intuitive way with many deduction details to encourage more researchers into this field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 9 references that are in the map. First posted on arXiv 3 Jan 2021.

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4 of 9 references.

References · 4

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2013Auto-Encoding Variational Bayescited by 4,053
  4. 2016Tutorial on Variational Autoencoderscited by 409

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