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Artificial Neural Networks and Machine Learning – ICANN 2020

29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Igor Farkaš

    1. Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

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  2. Paolo Masulli

    1. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

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    You can also search for this editor in PubMed   Google Scholar

  3. Stefan Wermter

    1. Department of Informatics, University of Hamburg, Hamburg, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12397)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICANN 2020.

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About this book

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.*

The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action.

*The conference was postponed to 2021 due to the COVID-19 pandemic.

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Table of contents (70 papers)

  1. Front Matter

    Pages i-xxvii
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  2. Model Compression I

    1. Front Matter

      Pages 1-1
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    2. Fine-Grained Channel Pruning for Deep Residual Neural Networks

      • Siang Chen, Kai Huang, Dongliang Xiong, Bowen Li, Luc Claesen
      Pages 3-14

    3. A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection

      • Yajie Li, Yiqiang Chen, Yang Gu, Jianquan Ouyang, Jiwei Wang, Ni Zeng
      Pages 15-26

    4. Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling

      • Tomoyuki Myojin, Shintaro Hashimoto, Naoki Ishihama
      Pages 27-38

    5. Neural Network Compression via Learnable Wavelet Transforms

      • Moritz Wolter, Shaohui Lin, Angela Yao
      Pages 39-51

    6. Fast and Robust Compression of Deep Convolutional Neural Networks

      • Jia Wen, Liu Yang, Chenyang Shen
      Pages 52-63

  3. Model Compression II

    1. Front Matter

      Pages 65-65
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    2. Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minima

      • Enzo Tartaglione, Andrea Bragagnolo, Marco Grangetto
      Pages 67-78

    3. Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks

      • Philipp Grüning, Thomas Martinetz, Erhardt Barth
      Pages 79-91

    4. Tuning Deep Neural Network’s Hyperparameters Constrained to Deployability on Tiny Systems

      • Riccardo Perego, Antonio Candelieri, Francesco Archetti, Danilo Pau
      Pages 92-103

    5. Obstacles to Depth Compression of Neural Networks

      • Will Burstein, John Wilmes
      Pages 104-115

  4. Multi-task and Multi-label Learning

    1. Front Matter

      Pages 117-117
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    2. Multi-label Quadruplet Dictionary Learning

      • Jiayu Zheng, Wencheng Zhu, Pengfei Zhu
      Pages 119-131

    3. Pareto Multi-task Deep Learning

      • Salvatore D. Riccio, Deyan Dyankov, Giorgio Jansen, Giuseppe Di Fatta, Giuseppe Nicosia
      Pages 132-141

    4. Convex Graph Laplacian Multi-Task Learning SVM

      • Carlos Ruiz, Carlos M. Alaíz, José R. Dorronsoro
      Pages 142-154

  5. Neural Network Theory and Information Theoretic Learning

    1. Front Matter

      Pages 155-155
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    2. Prediction Stability as a Criterion in Active Learning

      • Junyu Liu, Xiang Li, Jiqiang Zhou, Jianxiong Shen
      Pages 157-167

    3. Neural Spectrum Alignment: Empirical Study

      • Dmitry Kopitkov, Vadim Indelman
      Pages 168-179

    4. Nonlinear, Nonequilibrium Landscape Approach to Neural Network Dynamics

      • Roseli S. Wedemann, Angel R. Plastino
      Pages 180-191
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Other volumes

  1. Artificial Neural Networks and Machine Learning – ICANN 2020

  2. Artificial Neural Networks and Machine Learning – ICANN 2020

Editors and Affiliations

  • Department of Applied Informatics, Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  • Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark

    Paolo Masulli

  • Department of Informatics, University of Hamburg, Hamburg, Germany

    Stefan Wermter

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