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Lifelong learning in artificial neural networks

Published:21 May 2019Publication History
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Abstract

New methods enable systems to rapidly, continuously adapt.

References

  1. Chang, O. and Lipson, H. Neural Network Quine, Data Science Institute, Columbia University, New York, NY 10027, May 2018 https://arxiv.org/abs/1803.05859v3.Google ScholarGoogle Scholar
  2. Chen, Z. and Liu, B. Lifelong Machine Learning, Second Edition, Synthesis Lectures on Artificial Intelligence and Machine Learning, August 2018 https://www.morganclaypool.com/doi/10.2200/S00832ED1V01Y201802AIM037. Google ScholarGoogle ScholarCross RefCross Ref
  3. Hebb, D. The Organization of Behavior: A Neuropsychological Theory, New York: Wiley & Sons, 1949 http://s-f-walker.org.uk/pubsebooks/pdfs/The_Organization_of_Behavior-Donald_O._Hebb.pdf.Google ScholarGoogle Scholar
  4. Miconi, T., Clune, J., and Stanley, K. Differentiable Plasticity: Training Plastic Neural Networks with Backpropagation, Proceedings of the 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden, PMLR 80, 2018 https://arxiv.org/abs/1804.02464.Google ScholarGoogle Scholar
  5. Miconi, T. Backpropagation of Hebbian Plasticity for Continual Learning, NIPS Workshop on Continual Learning, 2016 https://github.com/ThomasMiconi/LearningToLearnBOHP/blob/master/paper/abstract.pdf.Google ScholarGoogle Scholar

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  1. Lifelong learning in artificial neural networks

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    • Published in

      cover image Communications of the ACM
      Communications of the ACM  Volume 62, Issue 6
      June 2019
      85 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/3336127
      Issue’s Table of Contents

      Copyright © 2019 ACM

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      New York, NY, United States

      Publication History

      • Published: 21 May 2019

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