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 ...
Read More
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.
Read Less
Add this copy of Tree-Based Convolutional Neural Networks: Principles to cart. $56.35, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2018 by Springer.
Add this copy of Tree-Based Convolutional Neural Networks: Principles to cart. $84.90, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2018 by Springer.
