# Dynamic Cell Structure via Recursive-Recurrent Neural Networks

## TL;DR

This paper introduces a dynamic neural architecture search method that constructs customized cell structures for each data sample and time step, improving efficiency and accuracy in recurrent neural networks.

## Contribution

It presents a novel recursive-recurrent neural network algorithm that dynamically searches for optimal cell structures tailored to individual data samples.

## Key findings

- Achieves higher prediction accuracy than GRU in language modeling.
- Discovers high-performance cell architectures through experiments.
- Demonstrates efficiency in architecture search for recurrent models.

## Abstract

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neural networks, our algorithm is able to construct customized cell structures for each data sample and time step, allowing for a more efficient architecture search than existing models. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better prediction accuracy compared to the GRU structure for language modelling and sentiment analysis.