Neural expectation maximization

Klaus Greff; Sjoerd Van Steenkiste; Jürgen Schmidhuber

Neural expectation maximization

Klaus Greff, Sjoerd Van Steenkiste, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings
Publisher	International Conference on Learning Representations, ICLR
State	Published - Jan 1 2019
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

Cite this

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title = "Neural expectation maximization",

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.",

author = "Klaus Greff and {Van Steenkiste}, Sjoerd and J{\"u}rgen Schmidhuber",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-14",

year = "2019",

month = jan,

day = "1",

language = "English (US)",

booktitle = "5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings",

publisher = "International Conference on Learning Representations, ICLR",

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TY - GEN

T1 - Neural expectation maximization

AU - Greff, Klaus

AU - Van Steenkiste, Sjoerd

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85083950575&partnerID=8YFLogxK

M3 - Conference contribution

BT - 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings

PB - International Conference on Learning Representations, ICLR

ER -

Neural expectation maximization

Abstract

Bibliographical note

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