Corpus ID: 211132493

Jelly Bean World: A Testbed for Never-Ending Learning

@article{Platanios2020JellyBW,
  title={Jelly Bean World: A Testbed for Never-Ending Learning},
  author={Emmanouil Antonios Platanios and Abulhair Saparov and Tom Mitchell},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.06306}
}
  • Emmanouil Antonios Platanios, Abulhair Saparov, Tom Mitchell
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Machine learning has shown growing success in recent years. However, current machine learning systems are highly specialized, trained for particular problems or domains, and typically on a single narrow dataset. Human learning, on the other hand, is highly general and adaptable. Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related… CONTINUE READING

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