气候变化领域集成服务门户(CCPortal): Density estimation using deep generative neural networks

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DOI	10.1073/pnas.2101344118
	Density estimation using deep generative neural networks
	Liu Q.; Xu J.; Jiang R.; Wong W.H.
发表日期	2021
ISSN	00278424
卷号	118 期号:15
英文摘要	Density estimation is one of the fundamental problems in both statistics and machine learning. In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks. Roundtrip retains the generative power of deep generative models, such as generative adversarial networks (GANs) while it also provides estimates of density values, thus supporting both data generation and density estimation. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings where target density is modeled by learning a manifold induced from a base density (e.g., Gaussian distribution). Roundtrip provides a statistical framework for GAN models where an explicit evaluation of density values is feasible. In numerical experiments, Roundtrip exceeds state-of-the-art performance in a diverse range of density estimation tasks. © This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND).
英文关键词	Deep learning; Density estimation; GAN; Importance sampling; Neural network
语种	英语
scopus关键词	article; artificial neural network; deep learning
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179924
作者单位	Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics, The Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China; Department of Statistics, Stanford University, Stanford, CA 94305, United States; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, United States; Bio-X Program, Stanford University, Stanford, CA 94305, United States; Center for Statistical Science, Tsinghua University, Beijing, 100084, China; Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China
推荐引用方式 GB/T 7714	Liu Q.,Xu J.,Jiang R.,et al. Density estimation using deep generative neural networks[J],2021,118(15).
APA	Liu Q.,Xu J.,Jiang R.,&Wong W.H..(2021).Density estimation using deep generative neural networks.Proceedings of the National Academy of Sciences of the United States of America,118(15).
MLA	Liu Q.,et al."Density estimation using deep generative neural networks".Proceedings of the National Academy of Sciences of the United States of America 118.15(2021).