Neural Expectation MaximizationDownload PDF

14 Jun 2024 (modified: 15 Mar 2017)ICLR 2017 workshop submissionReaders: 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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