摘要翻译：
由于几十年来积累了大量的测序数据，而且还在不断积累，我们需要处理的测序数据越来越多。随着计算技术的飞速发展，基于神经网络的模型可以在合理的时间内处理大量的数据。本教程将介绍单细胞变分推理(scVI)的数学模型，它使用变分自动编码器（建立在神经网络上）来学习数据的分布以获得洞察力。它以简单直观的方式为初学者编写，有许多推演细节，以鼓励更多的研究者进入这一领域。
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英文标题：
《A Tutorial on the Mathematical Model of Single Cell Variational
  Inference》
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作者：
Songting Shi
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最新提交年份：
2021
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Biology        数量生物学
二级分类：Genomics        基因组学
分类描述：DNA sequencing and assembly; gene and motif finding; RNA editing and alternative splicing; genomic structure and processes (replication, transcription, methylation, etc); mutational processes.
DNA测序与组装；基因和基序的发现；RNA编辑和选择性剪接；基因组结构和过程（复制、转录、甲基化等）；突变过程。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2101.00650