NIPS is a single-track machine learning and computational neuroscience conference that includes invited talks, demonstrations and oral and poster presentations of refereed papers.

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Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum and Shixiang Gu and Honglak Lee and Sergey Levine
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, cs.AI, stat.ML

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Summary by Felipe Martins 3 years ago
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Recurrent World Models Facilitate Policy Evolution
David Ha and Jürgen Schmidhuber
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, stat.ML

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Summary by Paul Barde 3 years ago
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Summary by David Stutz 3 years ago
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Summary by David Stutz 3 years ago
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Summary by David Stutz 3 years ago
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Summary by David Stutz 3 years ago
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Summary by Gavin Gray 4 years ago
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Summary by Hadrien Bertrand 4 years ago
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Summary by David Stutz 4 years ago
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Summary by David Stutz 4 years ago
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Summary by David Stutz 4 years ago
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Summary by David Stutz 4 years ago
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Summary by CodyWild 4 years ago
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Summary by David Stutz 5 years ago
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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Rosanne Liu and Joel Lehman and Piero Molino and Felipe Petroski Such and Eric Frank and Alex Sergeev and Jason Yosinski
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.CV, cs.LG, stat.ML

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Summary by CodyWild 5 years ago
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The challenge of realistic music generation: modelling raw audio at scale
Sander Dieleman and Aäron van den Oord and Karen Simonyan
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.SD, cs.LG, eess.AS, stat.ML

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Summary by CodyWild 5 years ago
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Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos and Maithra Raghu and Samy Bengio
arXiv e-Print archive - 2018 via Local arXiv
Keywords: stat.ML, cs.AI, cs.CV, cs.LG, cs.NE

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Summary by CodyWild 5 years ago
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