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Title: Improving Variational Autoencoders on Robustness, Regularization, and Task-Invariance
Other Titles: ロバスト性,正則化,タスク不変性に関する変分オートエンコーダの改善
Authors: Hiroshi, Takahashi
Author's alias: 高橋, 大志
Keywords: Variatinoal Autoencoder
Deep Generative Model
Unsupervised Learning
Representation Learning
Deep Learning
Issue Date: 23-Mar-2023
Publisher: Kyoto University
Conferring University: 京都大学
Degree Level: 新制・課程博士
Degree Discipline: 博士(情報学)
Degree Report no.: 甲第24725号
Degree no.: 情博第813号
Conferral date: 2023-03-23
Degree Call no.: 新制||情||137(附属図書館)
Degree Affiliation: 京都大学大学院情報学研究科知能情報学専攻
Examination Committee members: (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 吉川 正俊
Provisions of the Ruling of Degree: 学位規則第4条第1項該当
Rights: 3章は同著者らによる論文 1.Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018. と 2. 人工知能学会論文誌, 2021 年 36 巻 3 号 p. A-KA4_1-9. に基づく。Copyright (C) 2018, IJCAI. URL: 4章は同著者らによる論文 1. Proceedings of the 33-rd AAAI Conference on Artificial Intelligence , 2019. に基づく。Copyright (C) 2019, Association for the Advancement of Artificial Intelligence. URL: 5章は同著者らによる 論文1.Proceedings of the 28-th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022. に基づく。Copyright (C) 2022 ACM, Inc. URL:
DOI: 10.14989/doctor.k24725
Appears in Collections:140 Doctoral Dissertation (Informatics)

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