Tree-Based Convolutional Neural Networks

Principles and Applications

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Tree-Based Convolutional Neural Networks by Lili Mou, Zhi Jin, Springer Singapore
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Author: Lili Mou, Zhi Jin ISBN: 9789811318702
Publisher: Springer Singapore Publication: October 1, 2018
Imprint: Springer Language: English
Author: Lili Mou, Zhi Jin
ISBN: 9789811318702
Publisher: Springer Singapore
Publication: October 1, 2018
Imprint: Springer
Language: English

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data. TBCNNsare 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.

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