Overview
- Gathers tutorials from the 2019 INNS Big Data and Deep Learning Conference
- Describes cutting-edge AI-based tools and applications
- Offers essential guidance on the design and analysis of advanced AI-based models
- Blends mathematical foundations and practice-oriented insights
Part of the book series: Studies in Computational Intelligence (SCI, volume 896)
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Table of contents (8 chapters)
Keywords
- Deep Learning for Graphs
- Feedforward neural networks
- Applications of tensor decomposition
- Continual learning models
- Learned data structures
- Causal bayesian networks
- Online continual learning
- Deep learning models
- Sparsity of shallow networks
- Growing neural networks
- Searching problems
- Learned indexes
- Random weights
- Kruskal tensor
- Tucker form with multilinear rank
About this book
Editors and Affiliations
Bibliographic Information
Book Title: Recent Trends in Learning From Data
Book Subtitle: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)
Editors: Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-43883-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-43882-1Published: 04 April 2020
Softcover ISBN: 978-3-030-43885-2Published: 04 April 2021
eBook ISBN: 978-3-030-43883-8Published: 03 April 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: VII, 221
Number of Illustrations: 26 b/w illustrations, 55 illustrations in colour
Topics: Computational Intelligence, Machine Learning, Data Engineering