Titlebook: Recent Trends in Learning From Data; Tutorials from the I Luca Oneto,Nicolò Navarin,Davide Anguita Book 2020 The Editor(s) (if applicable)

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Deep Learning for Graphs, the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.

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1860-949X 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. 978-3-030-43885-2978-3-030-43883-8Series ISSN 1860-949X Series E-ISSN 1860-9503

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Paolo Ferragina,Giorgio Vinciguerra. Instead, Hay suggests that dance artists use language as a tool for both sensing micromovements in their bodies developed through years of dance training and for instructing audiences, producers, and critics on the significant place of language in dance.

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Ilya Kisil,Giuseppe G. Calvi,Bruno Scalzo Dees,Danilo P. Mandich (2. = 256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.

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Davide Bacciu,Alessio Michelih (2. = 256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.