Attention Augmented Learning Machines: Theory and Applications


Guoqiang Zhong, PhD (Editor) -Professor, Computer Science and Technology, Ocean University of China, Qingdao, China
Jinxuan Sun, PhD Student (Editor) – Computer Science and Technology, Ocean University of China, Qingdao, China

Series: Computer Science, Technology and Applications
BISAC: COM094000; COM044000; COM016000

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.

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Table of Contents


Book Description

Chapter 1. The Attention Mechanism Used in the Deep Learning Area
Jiajia Dong, Guoqiang Zhong, Zhaoyang Niu and Hui Yu

Chapter 2. Recurrent Attention Unit
Guoqiang Zhong, Guohua Yue, Zhaoyang Niu, and Xiao Ling

Chapter 3. Attention Mechanism-Based Long Short-Term Memory Model
Guoqiang Zhong, Xin Lin, Kang Chen, and Qingyang Li

Chapter 4. Industrial Surface Defect Detection Based on Multi-Attention Fusion Mechanism and Federated Learning
Xiaoli Yue and Guoqiang Zhong

Chapter 5. A Compact Object Detection Architecture with Transformer Enhancing
Liyuan Cui, Guoqiang Zhong, Xiang Liu, and Hongwei Xu

Chapter 6. Dual-Path Mutual Attention Network for Medical Image Classification
Yixuan Lu, Guoqiang Zhong and Yajing An

Chapter 7. AGGNN: Attention-Gated Graph Neural Network for Text Classification
Ke Xu, Guoqiang Zhong, Zhaoyang Deng, Chenxiang Sun, and Yuxu Mao

Chapter 8 Stock Market Prediction Based on LSTA-GAN
Hao Li, Daewon Choi, and Guoqiang Zhong



Editor’s ORCID iD

Guoqiang Zhong0000-0002-2952-6642

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