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Deep Learning models are at the core of artificial intelligence
research today. It is well known that deep learning techniques are
disruptive for Euclidean data, such as images or sequence data, and
not immediately applicable to graph-structured data such as text.
This gap has driven a wave of research for deep learning on graphs,
including graph representation learning, graph generation, and
graph classification. The new neural network architectures on
graph-structured data (graph neural networks, GNNs in short) have
performed remarkably on these tasks, demonstrated by applications
in social networks, bioinformatics, and medical informatics.
Despite these successes, GNNs still face many challenges ranging
from the foundational methodologies to the theoretical
understandings of the power of the graph representation learning.
This book provides a comprehensive introduction of GNNs. It first
discusses the goals of graph representation learning and then
reviews the history, current developments, and future directions of
GNNs. The second part presents and reviews fundamental methods and
theories concerning GNNs while the third part describes various
frontiers that are built on the GNNs. The book concludes with an
overview of recent developments in a number of applications using
GNNs. This book is suitable for a wide audience including
undergraduate and graduate students, postdoctoral researchers,
professors and lecturers, as well as industrial and government
practitioners who are new to this area or who already have some
basic background but want to learn more about advanced and
promising techniques and applications.
Deep Learning models are at the core of artificial intelligence
research today. It is well known that deep learning techniques are
disruptive for Euclidean data, such as images or sequence data, and
not immediately applicable to graph-structured data such as text.
This gap has driven a wave of research for deep learning on graphs,
including graph representation learning, graph generation, and
graph classification. The new neural network architectures on
graph-structured data (graph neural networks, GNNs in short) have
performed remarkably on these tasks, demonstrated by applications
in social networks, bioinformatics, and medical informatics.
Despite these successes, GNNs still face many challenges ranging
from the foundational methodologies to the theoretical
understandings of the power of the graph representation learning.
This book provides a comprehensive introduction of GNNs. It first
discusses the goals of graph representation learning and then
reviews the history, current developments, and future directions of
GNNs. The second part presents and reviews fundamental methods and
theories concerning GNNs while the third part describes various
frontiers that are built on the GNNs. The book concludes with an
overview of recent developments in a number of applications using
GNNs. This book is suitable for a wide audience including
undergraduate and graduate students, postdoctoral researchers,
professors and lecturers, as well as industrial and government
practitioners who are new to this area or who already have some
basic background but want to learn more about advanced and
promising techniques and applications.
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MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5-8, 2020, Proceedings, Part I (Paperback, 1st ed. 2020)
Yong Man Ro, Wen-Huang Cheng, Junmo Kim, Wei-Ta Chu, Peng Cui, …
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R3,650
Discovery Miles 36 500
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Ships in 10 - 15 working days
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The two-volume set LNCS 11961 and 11962 constitutes the thoroughly
refereed proceedings of the 25th International Conference on
MultiMedia Modeling, MMM 2020, held in Daejeon, South Korea, in
January 2020. Of the 171 submitted full research papers, 40 papers
were selected for oral presentation and 46 for poster presentation;
28 special session papers were selected for oral presentation and 8
for poster presentation; in addition, 9 demonstration papers and 6
papers for the Video Browser Showdown 2020 were accepted. The
papers of LNCS 11961 are organized in the following topical
sections: audio and signal processing; coding and HVS; color
processing and art; detection and classification; face; image
processing; learning and knowledge representation; video
processing; poster papers; the papers of LNCS 11962 are organized
in the following topical sections: poster papers; AI-powered 3D
vision; multimedia analytics: perspectives, tools and applications;
multimedia datasets for repeatable experimentation; multi-modal
affective computing of large-scale multimedia data; multimedia and
multimodal analytics in the medical domain and pervasive
environments; intelligent multimedia security; demo papers; and VBS
papers.
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