{"title":"自编码器增强的和积网络","authors":"Aaron W. Dennis, D. Ventura","doi":"10.1109/ICMLA.2017.00-13","DOIUrl":null,"url":null,"abstract":"Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 1","pages":"1041-1044"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Autoencoder-Enhanced Sum-Product Networks\",\"authors\":\"Aaron W. Dennis, D. Ventura\",\"doi\":\"10.1109/ICMLA.2017.00-13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.\",\"PeriodicalId\":6636,\"journal\":{\"name\":\"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"volume\":\"25 1\",\"pages\":\"1041-1044\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2017.00-13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2017.00-13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sum-product networks (SPNs) are probabilistic models that guarantee exact inference in time linear in the size of the network. We use autoencoders in concert with SPNs to model high-dimensional, high-arity random vectors (e.g., image data). Experiments show that our proposed model, the autoencoder-SPN (AESPN), which combines two SPNs and an autoencoder, produces better samples than an SPN alone. This is true whether we sample all variables, or whether a set of unknown query variables is sampled, given a set of known evidence variables.
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