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Selected Topics in Deep Learning and...
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Wang, Baiyang.
Selected Topics in Deep Learning and Text Mining.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Selected Topics in Deep Learning and Text Mining./
作者:
Wang, Baiyang.
面頁冊數:
1 online resource (114 pages)
附註:
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
標題:
Industrial engineering. -
電子資源:
click for full text (PQDT)
ISBN:
9780355824551
Selected Topics in Deep Learning and Text Mining.
Wang, Baiyang.
Selected Topics in Deep Learning and Text Mining.
- 1 online resource (114 pages)
Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Thesis (Ph.D.)--Northwestern University, 2018.
Includes bibliographical references
The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the economic background. The second chapter, "Regularization for Unsupervised Deep Neural Nets'', discusses and compares different regularization methods for unsupervised deep neural nets, such as deep belief networks, and proposes a new approach to refine Dropout. The third chapter, "An Attention-Based Deep Net for Learning to Rank'', proposes a list-wise attention-based learning-to-rank mechanism for image and document retrieval. The fourth chapter, "Generative Adversarial Nets for Multiple Text Corpora'', proposes two applications of the generative adversarial net on text data with multiple corpora, i.e. refining word embeddings and generating multi-corpora document embeddings.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355824551Subjects--Topical Terms:
679492
Industrial engineering.
Index Terms--Genre/Form:
554714
Electronic books.
Selected Topics in Deep Learning and Text Mining.
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Includes bibliographical references
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The thesis contains all four chapters of my Ph.D. research on deep learning and text mining. The first chapter, "Temporal Topic Analysis with Endogenous and Exogenous Processes'', proposes a topic model which mines temporal economy-related documents with an exogenous economic indicator, and finds the relationship between document topics and the economic background. The second chapter, "Regularization for Unsupervised Deep Neural Nets'', discusses and compares different regularization methods for unsupervised deep neural nets, such as deep belief networks, and proposes a new approach to refine Dropout. The third chapter, "An Attention-Based Deep Net for Learning to Rank'', proposes a list-wise attention-based learning-to-rank mechanism for image and document retrieval. The fourth chapter, "Generative Adversarial Nets for Multiple Text Corpora'', proposes two applications of the generative adversarial net on text data with multiple corpora, i.e. refining word embeddings and generating multi-corpora document embeddings.
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2018
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Mode of access: World Wide Web
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Industrial engineering.
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click for full text (PQDT)
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