# Learning Decision Trees Recurrently Through Communication

## TL;DR

This paper introduces a recurrent neural network-based decision tree model that iteratively makes binary decisions, offering interpretable, sparse, and accurate predictions with semantic rationalizations, validated on large-scale image datasets.

## Contribution

The paper presents a novel recurrent neural network model that learns decision trees with semantic binary decisions, enhancing interpretability without sacrificing accuracy.

## Key findings

- Achieves state-of-the-art accuracy on ImageNet
- Provides human-interpretable binary decision sequences
- Builds decision trees encoded in RNN memory through message passing

## Abstract

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.