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Continual learning for recurrent neural networks: An empirical evaluation

Andrea Cossu; Antonio Carta; Vincenzo Lomonaco; Davide Bacciu;
Open Access English
Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contribut...
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A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence
  • Funder: European Commission (EC)
  • Project Code: 871385
  • Funding stream: Horizon 2020 Framework Programme - Research and Innovation action
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