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Jun, 2020
缺失数据下的VAEs
VAEs in the Presence of Missing Data
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Mark Collier, Alfredo Nazabal, Christopher K. I. Williams
TL;DR
开发了一种新颖的潜在变量模型,通过生成缺失数据的损坏过程对处理缺失数据集进行了模糊处理,并推导出相应的易于实现、可处理可随机缺失和不随机缺失数据、适用于高维输入、VAE编码器和解码器原则性访问指标变量以确定数据元素是否缺失的可跟踪证据下限(ELBO)。在MNIST和SVHN数据集上,相比现有方法,证明了观测数据的边际对数似然和更好的缺失数据插值提高。
Abstract
Real world datasets often contain entries with missing elements e.g. in a medical dataset, a patient is unlikely to have taken all possible diagnostic tests.
variational autoencoders
(VAEs) are popular generative models often used for
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