| Description |
1 online resource |
| Series |
SpringerBriefs in computer science |
|
SpringerBriefs in computer science, 2191-5768
|
| Contents |
Introduction -- Preliminaries and Related Work -- General Concepts of Tree-Based Convolutional Neural Networks (TBCNNs) -- TBCNN for Programs' Abstract Syntax Trees (ASTs) -- TBCNN for Constituency Trees in Natural Language Processing -- TBCNN for Dependency Trees in Natural Language Processing -- Concluding Remarks |
| Summary |
This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs), for processing tree-structured data. TBCNNs are related to existing convolutional neural networks (CNNs) and recursive neural networks (RNNs), but they combine the merits of both: thanks to their short propagation path, they are as efficient in learning as CNNs; yet they are also as structure-sensitive as RNNs. In this book, readers will also find a comprehensive literature review of related work, detailed descriptions of TBCNNs and their variants, and experiments applied to program analysis and natural language processing tasks. It is also an enjoyable read for all those with a general interest in deep learning |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on July 10, 2019) |
| Subject |
Neural networks (Computer science)
|
|
Machine learning.
|
|
Artificial intelligence -- Data processing.
|
|
Neural Networks, Computer
|
|
Machine Learning
|
|
Data mining.
|
|
Artificial intelligence.
|
|
Software Engineering.
|
|
COMPUTERS -- General.
|
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Neural networks (Computer science)
|
|
Machine learning
|
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Artificial intelligence -- Data processing
|
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Artificial intelligence
|
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Data mining
|
|
Engineering
|
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Software engineering
|
| Form |
Electronic book
|
| Author |
Jin, Zhi, author.
|
| ISBN |
9789811318702 |
|
9811318700 |
|
