Attention Augmented Learning Machines: Theory and Applications
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| Title: | Attention Augmented Learning Machines: Theory and Applications |
|---|---|
| Description: | This book includes eight chapters introducing some interesting works on the attention mechanism. Chapter 1 is a review of the attention mechanism used in the deep learning area, while Chapter 2 and Chapter 3 present two models that integrate the attention mechanism into gated recurrent units (GRUs) and long short-term memory (LSTM), respectively, making them pay attention to important information in the sequences. Chapter 4 designs a multi-attention fusion mechanism and uses it for industrial surface defect detection. Chapter 5 enhances Transformer for object detection applications. Moreover, Chapter 6 proposes a dual-path architecture called dual-path mutual attention network (DPMAN) for medical image classification, and Chapter 7 proposes a novel graph model called attention-gated graph neural network (AGGNN) for text classification. In addition, Chapter 8 combines the generative adversarial networks (GANs), LSTM, and an attention mechanism to build a generative model for stock price prediction. |
| Authors: | Guoqiang Zhong |
| Resource Type: | eBook. |
| Subjects: | Machine learning, Big data, Deep learning (Machine learning), Attention--Computer simulation |
| Categories: | COMPUTERS / Data Science / Machine Learning |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Attention Augmented Learning Machines: Theory and Applications – Name: Abstract Label: Description Group: Ab Data: This book includes eight chapters introducing some interesting works on the attention mechanism. Chapter 1 is a review of the attention mechanism used in the deep learning area, while Chapter 2 and Chapter 3 present two models that integrate the attention mechanism into gated recurrent units (GRUs) and long short-term memory (LSTM), respectively, making them pay attention to important information in the sequences. Chapter 4 designs a multi-attention fusion mechanism and uses it for industrial surface defect detection. Chapter 5 enhances Transformer for object detection applications. Moreover, Chapter 6 proposes a dual-path architecture called dual-path mutual attention network (DPMAN) for medical image classification, and Chapter 7 proposes a novel graph model called attention-gated graph neural network (AGGNN) for text classification. In addition, Chapter 8 combines the generative adversarial networks (GANs), LSTM, and an attention mechanism to build a generative model for stock price prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Guoqiang+Zhong%22">Guoqiang Zhong</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning+%28Machine+learning%29%22">Deep learning (Machine learning)</searchLink><br /><searchLink fieldCode="DE" term="%22Attention--Computer+simulation%22">Attention--Computer simulation</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Machine+Learning%22">COMPUTERS / Data Science / Machine Learning</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.3101 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Big data Type: general – SubjectFull: Deep learning (Machine learning) Type: general – SubjectFull: Attention--Computer simulation Type: general Titles: – TitleFull: Attention Augmented Learning Machines: Theory and Applications Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Guoqiang Zhong – PersonEntity: Name: NameFull: Guoqiang Zhong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 – D: 16 M: 11 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9798886977806 – Type: isbn-print Value: 9798891131620 – Type: isbn-electronic Value: 9798891131613 Titles: – TitleFull: Attention Augmented Learning Machines: Theory and Applications Type: main |
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