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|