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010 a| 2023037936;

020 a| 9798886977806q| (paperback);

020 z| 9798891131613q| (pdf);

020 z| 979891131620q| (epub);

035 a| (WaSeSS)ssj0002966832;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| QA76.9.B45b| A88 2023;

082 00 a| 006.3/1012| 23/eng/20240130;

245 00 a| Attention augmented learning machinesh| [electronic resource] :b| theory and applications /c| Guoqiang Zhong and Jinxuan Sun.;

260 a| New York :b| Nova Science Publishers,c| [2023];

300 a| viii, 126 pages ;c| 25 cm.;

490 0 a| Computer science, technology and applications;

504 a| Includes bibliographical references and index.;

505 2 a| The attention mechanism used in the deep learning area / Jiajia Dong, Guoqiang Zhong, Zhaoyang Niu and Hui Yu -- Recurrent attention unit / Guoqiang Zhong, Guohua Yue, Zhaoyang Niu, and Xiao Ling.;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Big data.=| ^A1157100;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Attentionx| Computer simulation.=| ^A21434;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Zhong, Guoqiangc| (Professor of computer science)?| UNAUTHORIZED;

700 1 a| Sun, Jinxuan.?| UNAUTHORIZED;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=30678266;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

949 a| CLICK ON WEB ADDRESSw| ASISh| HSL77;

949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 6742291;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6742291-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 7/15/2026x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6742291-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 7/15/2026x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6742291-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/15/2026x| EBOOKz| JERESOURCE;

Attention augmented learning machines theory and applications / Guoqiang Zhong and Jinxuan Sun.

Other author	Zhong, Guoqiang (Professor of computer science)
Other author	Sun, Jinxuan.
Format	Electronic
Publication Info	New York : Nova Science Publishers, [2023]
Description	viii, 126 pages ; 25 cm.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Big data. Machine learning. Attention--Computer simulation.

Series	Computer science, technology and applications
Partial contents	The attention mechanism used in the deep learning area / Jiajia Dong, Guoqiang Zhong, Zhaoyang Niu and Hui Yu -- Recurrent attention unit / Guoqiang Zhong, Guohua Yue, Zhaoyang Niu, and Xiao Ling.
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2023037936
ISBN	9798886977806 (paperback)
ISBN	(pdf)
ISBN	(epub)

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Attention augmented learning machines theory and applications / Guoqiang Zhong and Jinxuan Sun.

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