Foundations and Trends in Machine Learning最新文献

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Model-based Reinforcement Learning: A Survey 基于模型的强化学习:综述
Foundations and Trends in Machine Learning Pub Date : 2023-01-01 DOI: 10.1561/2200000086
Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
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引用次数: 84
Probabilistic Learning 概率学习
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_6
T. Jo
{"title":"Probabilistic Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_6","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_6","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"4 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75335216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data Encoding 数据编码
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_3
T. Jo
{"title":"Data Encoding","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_3","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_3","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"1 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88609756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Support Vector Machine 支持向量机
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_8
T. Jo
{"title":"Support Vector Machine","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_8","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_8","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"30 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81512838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Numerical Vectors 数值向量
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_2
T. Jo
{"title":"Numerical Vectors","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_2","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_2","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"108 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86992067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Advanced Clustering 先进的集群
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_12
T. Jo
{"title":"Advanced Clustering","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_12","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_12","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"57 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82258178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Temporal Learning 时间学习
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_15
T. Jo
{"title":"Temporal Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_15","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_15","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"45 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85906006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Semi-supervised Learning Semi-supervised学习
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_14
T. Jo
{"title":"Semi-supervised Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_14","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_14","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"9 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84571505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reinforcement Learning 强化学习
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-12-01 DOI: 10.1007/978-3-030-65900-4_16
T. Jo
{"title":"Reinforcement Learning","authors":"T. Jo","doi":"10.1007/978-3-030-65900-4_16","DOIUrl":"https://doi.org/10.1007/978-3-030-65900-4_16","url":null,"abstract":"","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"364 1","pages":""},"PeriodicalIF":32.8,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80304699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
How Good Is Your Scientific Data Generative Model? 你的科学数据生成模型有多好?
IF 32.8
Foundations and Trends in Machine Learning Pub Date : 2020-11-01 DOI: 10.1109/MLHPCAI4S51975.2020.00018
Yuxin Yang, Ben Gremillion, Xitong Zhang, Youzuo Lin, B. Wohlberg, Qiang Guan
{"title":"How Good Is Your Scientific Data Generative Model?","authors":"Yuxin Yang, Ben Gremillion, Xitong Zhang, Youzuo Lin, B. Wohlberg, Qiang Guan","doi":"10.1109/MLHPCAI4S51975.2020.00018","DOIUrl":"https://doi.org/10.1109/MLHPCAI4S51975.2020.00018","url":null,"abstract":"Nowadays, leveraging data augmentation methods on helping resolving scientific problems becomes prevailing. And many scientific problems benefit from data augmentation methods build with deep generative models. Yet due to the complexity of the scientific data, commonly used evaluation methods of generative models appear not so suitable for generated scientific data. In this paper, we explore how do we effectively evaluate data augmentation methods for scientific data generative models? To answer this question, we use one example of real world scientific problem to show how we evaluate the quality of the generated data from two domain specific deep generative models. We observe that most existing state-of-art evaluation metrics are incompetent. They either show completely contradicting results or provide inaccurate insight from real data.","PeriodicalId":47667,"journal":{"name":"Foundations and Trends in Machine Learning","volume":"56 1","pages":"96-102"},"PeriodicalIF":32.8,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75362366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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