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Continual Unsupervised Representation Learning

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DeepMind: How can we learn a sequence of tasks without forgetting, without class labels and with unknown or ambiguous task boundaries? Continual Unsupervised Representation Learning: Paper: https://arxiv.org/abs/1910.14481 Code: https://github.com/deepmind/deepmind-research/tree/master/curl https://t.co/3WSzWmILlB

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DeepMind: How can we learn a sequence of tasks without forgetting, without class labels and with unknown or ambiguous task boundaries? Continual Unsupervised Representation Learning Paper: https://arxiv.org/abs/1910.14481 Code: https://github.com/deepmind/deepmind-research/tree/master/curl https://t.co/uo56hi2Gki

1 replies, 71 likes


Dushyant Rao: Excited to share our latest work! To appear at NeurIPS 2019 in a couple of weeks

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Markus Wulfmeier: Continual learning without task boundaries via dynamic expansion, generative replay & more. Great work by @drao64 et al.

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arXiv in review: #NeurIPS2019 Continual Unsupervised Representation Learning. (arXiv:1910.14481v1 [cs\.LG]) http://arxiv.org/abs/1910.14481

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AI Papers: Continual Unsupervised Representation Learning. http://arxiv.org/abs/1910.14481

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Lantern Institute: Continual Unsupervised Representation Learning #ArtificialIntelligence #Continual #ai via http://twinybots.ch https://arxiv.org/abs/1910.14481

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Sébastien Loustau: 🔥✍

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Content

Found on Nov 22 2019 at https://arxiv.org/pdf/1910.14481.pdf

PDF content of a computer science paper: Continual Unsupervised Representation Learning