Recent Advances in Big Data and Deep Learning

Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019
Herausgegeben:Oneto, Luca; Navarin, Nicolò; Sperduti, Alessandro; Anguita, Davide

• Broschiertes Buch

Produktbeschreibung

This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.

Produktdetails

• Proceedings of the International Neural Networks Society 1
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-16840-7
• 1st ed. 2020
• Seitenzahl: 404
• Erscheinungstermin: 3. April 2019
• Englisch
• Abmessung: 235mm x 155mm x 22mm
• Gewicht: 620g
• ISBN-13: 9783030168407
• ISBN-10: 3030168409
• Artikelnr.: 55357767

Inhaltsangabe

On the trade-o between number of examples and precision of supervision in regression.- Distributed SmSVM Ensemble Learning.- Size/Accuracy Trade-off in Convolutional Neural Networks: An Evolutionary Approach.- Fast transfer learning for image polarity detection.- Dropout for Recurrent Neural Networks.- Psychiatric disorders classification with 3D Convolutional Neural Networks.- Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions.- Deep-learning domain adaptation techniques for credit cards fraud detection.- Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks.- An information theoretic approach to the autoencoder.- Deep Regression Counting: Customized Datasets and Inter-Architecture Transfer Learning.- Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies.

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On the trade-off between number of examples and precision of supervision in regression.- Distributed SmSVM Ensemble Learning.- Size/Accuracy Trade-off in Convolutional Neural Networks: An Evolutionary Approach.- Fast transfer learning for image polarity detection.- Dropout for Recurrent Neural Networks.- Psychiatric disorders classification with 3D Convolutional Neural Networks.- Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions.- Deep-learning domain adaptation techniques for credit cards fraud detection.- Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks.- An information theoretic approach to the autoencoder.- Deep Regression Counting: Customized Datasets and Inter-Architecture Transfer Learning.- Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies.