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Machine Learning, Optimization, and Data Science

6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Giuseppe Nicosia

    1. University of Catania, Catania, Italy

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  2. Varun Ojha

    1. University of Reading, Reading, UK

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  3. Emanuele La Malfa

    1. University of Oxford, Oxford, UK

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  4. Giorgio Jansen

    1. University of Cambridge, Cambridge, UK

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  5. Vincenzo Sciacca

    1. Almawave, Rome, Italy

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  6. Panos Pardalos

    1. University of Florida, Gainesville, USA

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  7. Giovanni Giuffrida

    1. University of Catania, Catania, Italy

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  8. Renato Umeton

    1. Harvard University, Cambridge, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12566)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: LOD 2020.

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Table of contents (53 papers)

  1. Front Matter

    Pages i-xxxviii
    Download chapter PDF

  2. Multi-kernel Covariance Terms in Multi-output Support Vector Machines

    • Elisa Marcelli, Renato De Leone
    Pages 1-11

  3. Generative Fourier-Based Auto-encoders: Preliminary Results

    • Alessandro Zonta, Ali El Hassouni, David W. Romero, Jakub M. Tomczak
    Pages 12-15

  4. Parameterized Structured Pruning for Deep Neural Networks

    • Günther Schindler, Wolfgang Roth, Franz Pernkopf, Holger Fröning
    Pages 16-27

  5. FoodViz: Visualization of Food Entities Linked Across Different Standards

    • Riste Stojanov, Gorjan Popovski, Nasi Jofce, Dimitar Trajanov, Barbara Koroušić Seljak, Tome Eftimov
    Pages 28-38

  6. Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization

    • Mayumi Ohta, Nathaniel Berger, Artem Sokolov, Stefan Riezler
    Pages 39-64

  7. Learning Controllers for Adaptive Spreading of Carbon Fiber Tows

    • Julia Krützmann, Alexander Schiendorfer, Sergej Beratz, Judith Moosburger-Will, Wolfgang Reif, Siegfried Horn
    Pages 65-77

  8. Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent

    • Alper Yegenoglu, Kai Krajsek, Sandra Diaz Pier, Michael Herty
    Pages 78-92

  9. Effects of Random Seeds on the Accuracy of Convolutional Neural Networks

    • Christofer Fellicious, Thomas Weissgerber, Michael Granitzer
    Pages 93-102

  10. Benchmarking Deep Learning Models for Driver Distraction Detection

    • Jimiama Mafeni Mase, Peter Chapman, Grazziela P. Figueredo, Mercedes Torres Torres
    Pages 103-117

  11. Chronologically Guided Deep Network for Remaining Useful Life Estimation

    • Abhay Harpale
    Pages 118-130

  12. A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

    • Leandro L. Lorente-Leyva, M. M. E. Alemany, Diego H. Peluffo-Ordóñez, Israel D. Herrera-Granda
    Pages 131-142

  13. Automatic Curriculum Recommendation for Employees

    • Abhay Harpale
    Pages 143-155

  14. Target-Aware Prediction of Tool Usage in Sequential Repair Tasks

    • Nima Nabizadeh, Martin Heckmann, Dorothea Kolossa
    Pages 156-168

  15. Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing

    • Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
    Pages 169-180

  16. Coking Coal Railway Transportation Forecasting Using Ensembles of ElasticNet, LightGBM, and Facebook Prophet

    • Vladimir Soloviev, Nikita Titov, Elena Smirnova
    Pages 181-190

  17. Multi-parameter Regression of Photovoltaic Systems using Selection of Variables with the Method: Recursive Feature Elimination for Ridge, Lasso and Bayes

    • Jose Cruz, Wilson Mamani, Christian Romero, Ferdinand Pineda
    Pages 191-202

  18. High-Dimensional Constrained Discrete Multi-objective Optimization Using Surrogates

    • Rommel G. Regis
    Pages 203-214

  19. Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations

    • Jon Vadillo, Roberto Santana, Jose A. Lozano
    Pages 215-227

  20. Lottery Ticket Hypothesis: Placing the k-orrect Bets

    • Abhinav Raj, Subhankar Mishra
    Pages 228-239
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

Keywords

About this book

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.

The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Editors and Affiliations

  • University of Catania, Catania, Italy

    Giuseppe Nicosia, Giovanni Giuffrida

  • University of Reading, Reading, UK

    Varun Ojha

  • University of Oxford, Oxford, UK

    Emanuele La Malfa

  • University of Cambridge, Cambridge, UK

    Giorgio Jansen

  • Almawave, Rome, Italy

    Vincenzo Sciacca

  • University of Florida, Gainesville, USA

    Panos Pardalos

  • Harvard University, Cambridge, USA

    Renato Umeton

Bibliographic Information

  • Book Title: Machine Learning, Optimization, and Data Science

  • Book Subtitle: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II

  • Editors: Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Giorgio Jansen, Vincenzo Sciacca, Panos Pardalos, Giovanni Giuffrida, … Renato Umeton

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-64580-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-64579-3Published: 07 January 2021

  • eBook ISBN: 978-3-030-64580-9Published: 06 January 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXXVIII, 666

  • Number of Illustrations: 32 b/w illustrations, 179 illustrations in colour

  • Topics: Computer Applications, Machine Learning, Computers and Education, Artificial Intelligence, Computing Milieux

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