Neural Expectation Maximization

08/11/2017
by   Klaus Greff, et al.
0

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects. We demonstrate that the learned representations are useful for next-step prediction.

READ FULL TEXT

page 5

page 6

page 8

research
07/31/2019

Neural Network based Explicit Mixture Models and Expectation-maximization based Learning

We propose two neural network based mixture models in this article. The ...
research
02/07/2019

Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping

Humans perceive the seemingly chaotic world in a structured and composit...
research
11/19/2015

Binding via Reconstruction Clustering

Disentangled distributed representations of data are desirable for machi...
research
06/22/2018

Grouped Mixture of Regressions

Finite Mixture of Regressions (FMR) models are among the most widely use...
research
10/19/2020

Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding

In this paper, we study the problem of estimating latent variable models...
research
02/28/2018

Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

Common-sense physical reasoning is an essential ingredient for any intel...
research
09/29/2020

Identification of Probability weighted ARX models with arbitrary domains

Hybrid system identification is a key tool to achieve reliable models of...

Please sign up or login with your details

Forgot password? Click here to reset