# An Attentive Survey of Attention Models

## TL;DR

This survey provides a comprehensive overview of attention models in neural networks, categorizing techniques, architectures, applications, interpretability, and future directions to guide researchers and practitioners.

## Contribution

It introduces a taxonomy of attention techniques, reviews neural architectures incorporating attention, and discusses applications and interpretability improvements.

## Key findings

- Attention models have significantly impacted diverse neural network applications.
- A structured taxonomy helps organize existing attention techniques.
- Attention improves interpretability of neural networks.

## Abstract

Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated, and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.