Attention Augmented Learning Machines: Theory and Applications (eBook, PDF)

Redaktion: Guoqiang Zhong

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Nova Science Publishers Inc
• Seitenzahl: 140
• Erscheinungstermin: 19. September 2023
• Englisch
• ISBN-13: 9798891131613
• Artikelnr.: 72427688