# Dynamic Partition Models

## TL;DR

The paper introduces a dynamic partitioning approach for learning compact binary representations by assigning variables to the most reliable experts, enabling efficient high-dimensional data modeling.

## Contribution

It proposes a novel dynamic partition model that assigns variables to experts based on reliability, differing from traditional sum-based expert models.

## Key findings

- Accurate reconstruction of high-dimensional data
- Uses at most a dozen experts
- Dynamic partitioning improves interpretability

## Abstract

We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we employ for each variable the opinion of the most reliable expert. Data points are hence explained through a partitioning of the variables into expert supports. The partitions are dynamically adapted based on which experts are active. During the learning phase we adopt a smoothed version of this model that uses separate mixtures for each data dimension. In our experiments we achieve accurate reconstructions of high-dimensional data points with at most a dozen experts.