Attention Augmented Learning Machines: Theory and Applications
Published on 19. September 2023
140 pages
979-8-89113-161-3 (ISBN)
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.
More details
Content
- Intro
- Half Title
- Title Page
- Copyright Page
- Contents
- Preface
- Book Description
- Chapter 1: The Attention Mechanism Used in the Deep Learning Area
- Chapter 2: Recurrent Attention Unit
- Chapter 3: Attention Mechanism-Based Long Short-Term Memory Model
- Chapter 4: Industrial Surface Defect Detection Based on Multi-Attention Fusion Mechanism and Federated Learning
- Chapter 5: A Compact Object Detection Architecture with Transformer Enhancing
- Chapter 6: Dual-Path Mutual Attention Network for Medical Image Classification
- Chapter 7: AGGNN: Attention-Gated Graph Neural Network for Text Classification
- Chapter 8: Stock Market Prediction Based on LSTA-GAN
- Contributors
- Index
- Blank Page
- Blank Page
