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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

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Luca Oneto

    1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

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  2. Nicolò Navarin

    1. Department of Mathematics, University of Padova, Padua, Italy

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  3. Alessandro Sperduti

    1. Department of Mathematics, University of Padova, Padua, Italy

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  4. Davide Anguita

    1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

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  • Offers recent research in Big Data and Deep Learning
  • Presents contributions from researchers and professionals in Big Data, Deep Learning and related areas
  • Includes Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy, 16–18 April 2019
  • Is organized by the International Neural Network Society

Part of the book series: Proceedings of the International Neural Networks Society (INNS, volume 1)

Included in the following conference series:

Conference proceedings info: INNSBDDL 2019.

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Table of contents (39 papers)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. On the Trade-Off Between Number of Examples and Precision of Supervision in Regression

    • Giorgio Gnecco, Federico Nutarelli
    Pages 1-6

  3. Distributed SmSVM Ensemble Learning

    • Jeff Hajewski, Suely Oliveira
    Pages 7-16

  4. Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach

    • Tomaso Cetto, Jonathan Byrne, Xiaofan Xu, David Moloney
    Pages 17-26

  5. Fast Transfer Learning for Image Polarity Detection

    • Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino
    Pages 27-37

  6. Dropout for Recurrent Neural Networks

    • Nathan Watt, Mathys C. du Plessis
    Pages 38-47

  7. Psychiatric Disorders Classification with 3D Convolutional Neural Networks

    • Stefano Campese, Ivano Lauriola, Cristina Scarpazza, Giuseppe Sartori, Fabio Aiolli
    Pages 48-57

  8. Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions

    • Zhishen Huang, Stephen Becker
    Pages 58-77

  9. Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection

    • Bertrand Lebichot, Yann-AĂ«l Le Borgne, Liyun He-Guelton, FrĂ©dĂ©ric OblĂ©, Gianluca Bontempi
    Pages 78-88

  10. Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks

    • Hong-Jun Yoon, John X. Qiu, J. Blair Christian, Jacob Hinkle, Folami Alamudun, Georgia Tourassi
    Pages 89-98

  11. An Information Theoretic Approach to the Autoencoder

    • Vincenzo Crescimanna, Bruce Graham
    Pages 99-108

  12. Deep Regression Counting: Customized Datasets and Inter-Architecture Transfer Learning

    • Iam Palatnik de Sousa, Marley Maria Bernardes Rebuzzi Vellasco, Eduardo Costa da Silva
    Pages 109-119

  13. Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies

    • Roberto Spigolon, Luca Oneto, Dimitar Anastasovski, Nadia Fabrizio, Marie Swiatek, Renzo Canepa et al.
    Pages 120-125

  14. Presumable Applications of Deep Learning for Cellular Automata Identification

    • Anton Popov, Alexander Makarenko
    Pages 126-135

  15. Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability

    • Luca Oneto, Irene Buselli, Paolo Sanetti, Renzo Canepa, Simone Petralli, Davide Anguita
    Pages 136-141

  16. Train Overtaking Prediction in Railway Networks: A Big Data Perspective

    • Luca Oneto, Irene Buselli, Alessandro Lulli, Renzo Canepa, Simone Petralli, Davide Anguita
    Pages 142-151

  17. Cavitation Noise Spectra Prediction with Hybrid Models

    • Francesca Cipollini, Fabiana Miglianti, Luca Oneto, Giorgio Tani, Michele Viviani, Davide Anguita
    Pages 152-157

  18. Pseudoinverse Learners: New Trend and Applications to Big Data

    • Ping Guo, Dongbin Zhao, Min Han, Shoubo Feng
    Pages 158-168

  19. Innovation Capability of Firms: A Big Data Approach with Patents

    • Linda Ponta, Gloria Puliga, Luca Oneto, Raffaella Manzini
    Pages 169-179

  20. Predicting Future Market Trends: Which Is the Optimal Window?

    • Simone Merello, Andrea Picasso Ratto, Luca Oneto, Erik Cambria
    Pages 180-185
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Other volumes

  1. Recent Advances in Big Data and Deep Learning

Keywords

About this book

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.

 


Editors and Affiliations

  • Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Luca Oneto, Davide Anguita

  • Department of Mathematics, University of Padova, Padua, Italy

    Nicolò Navarin, Alessandro Sperduti

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