论文标题
使用变量自动编码器的潜在变量建模：调查
Latent Variable Modelling Using Variational Autoencoders: A survey
论文作者
论文摘要
概率分布使从业人员可以在数据中发现隐藏的结构，并构建模型，以使用有限的数据解决监督的学习问题。该报告的重点是变性自动编码器，这是一种学习大型复杂数据集概率分布的方法。该报告提供了对变异自动编码器的理论理解，并巩固了该领域的当前研究。 该报告分为多个章节，第一章介绍了问题，描述了变异自动编码器并标识了该领域的关键研究方向。第2、3、4和5章深入研究了每个关键研究领域的细节。第6章总结了报告，并提出了未来工作的指示。 具有机器学习基本思想但想了解机器学习研究中的一般主题的读者可以从报告中受益。该报告解释了有关学习概率分布的中心思想，人们为使这项处理方法做了什么，并详细介绍了当前如何应用深度学习的细节。该报告还为希望为这个子场做出贡献的人提供了温和的介绍。
A probability distribution allows practitioners to uncover hidden structure in the data and build models to solve supervised learning problems using limited data. The focus of this report is on Variational autoencoders, a method to learn the probability distribution of large complex datasets. The report provides a theoretical understanding of variational autoencoders and consolidates the current research in the field. The report is divided into multiple chapters, the first chapter introduces the problem, describes variational autoencoders and identifies key research directions in the field. Chapters 2, 3, 4 and 5 dive into the details of each of the key research areas. Chapter 6 concludes the report and suggests directions for future work. A reader who has a basic idea of machine learning but wants to learn about general themes in machine learning research can benefit from the report. The report explains central ideas on learning probability distributions, what people did to make this tractable and goes into details around how deep learning is currently applied. The report also serves a gentle introduction for someone looking to contribute to this sub-field.
