Recent Trends in Learning From Data

Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)

Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita (Herausgeber)

Buch | Softcover

VII, 221 Seiten

2021
Springer International Publishing (Verlag)
978-3-030-43885-2 (ISBN)

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This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

Introduction: Recent Trends in Learning From Data.- Learned data structures.- Deep Randomized Neural Networks.- Tensor Decompositions and Practical Applications.- Deep learning for graphs.- Limitations of Shallow Networks.- Fairness in Machine Learning.- Online Continual Learning on Sequences.

Erscheinungsdatum	08.04.2021
Reihe/Serie	Studies in Computational Intelligence
Zusatzinfo	VII, 221 p. 81 illus., 55 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	361 g
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Technik
Schlagworte	Applications of tensor decomposition • Causal bayesian networks • Continual learning models • Deep Learning for Graphs • Deep learning models • Feedforward Neural Networks • Growing neural networks • Kruskal tensor • Learned data structures • Learned indexes • Online continual learning • Random weights • Searching problems • Sparsity of shallow networks • Tucker form with multilinear rank
ISBN-10	3-030-43885-6 / 3030438856
ISBN-13	978-3-030-43885-2 / 9783030438852
Zustand	Neuware