Abstract
In this chapter, we provide a whirlwind introduction of the history of deep neural networks (also known as deep learning), positioned in a broader scope of machine learning and artificial intelligence. We then focus on a specific research direction of deep neural networks—incorporating structural information of data into the design of network architectures. This motivates the key contribution of the book, a tree-based convolutional neural network (TBCNN), that performs the convolution operation over tree structures. Finally, we provide an overview of this book.
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Notes
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In the literature, a recursive neural network is sometimes also abbreviated as RNN. However, this is confusingly the same as a recurrent neural network. We do not use this acronym for the recursive neural network.
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Mou, L., Jin, Z. (2018). Introduction. In: Tree-Based Convolutional Neural Networks. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-1870-2_1
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DOI: https://doi.org/10.1007/978-981-13-1870-2_1
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