Neural Expectation Maximization

Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber

Feb 17, 2017 (modified: Mar 15, 2017) ICLR 2017 workshop submission readers: everyone
  • Abstract: We introduce a novel framework for clustering that combines generalized EM with neural networks and can be implemented as an end-to-end differentiable recurrent neural network. It learns its statistical model directly from the data and can represent complex non-linear dependencies between inputs. We apply our framework to a perceptual grouping task and empirically verify that it yields the intended behavior as a proof of concept.
  • TL;DR: A framework for clustering that combines generalized EM with neural networks and can be implemented as an end-to-end differentiable recurrent neural network
  • Keywords: Theory, Deep learning, Unsupervised Learning
  • Conflicts: usi.ch, idsia.ch, supsi.ch, cai.fi

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