Awesome-Long-Sequence

Introduction

This repository summarize awesome methods of long-sequence modeling for representation learning in various scenarios.

• To find related papers and their relationships, check out Connected Papers, which visualizes the academic field in a graph representation.
• To export BibTeX citations of papers, check out ArXiv or Semantic Scholar of the paper for professional reference formats.

Table of Contents

Fundermental Methods

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Contribution

Feel free to send pull requests to add more links with the following Markdown format. Notice that the Abbreviation, the code link, and the figure link are optional attributes.

* **Abbreviation**: Author List.

  Paper Name. [[Conference'Year](link)] [[code](link)]
  <details close>
  <summary>Framework</summary>
  <p align="center"><img width="90%" src="link_to_image" /></p>
  </details>

Current contributors include: Siyuan Li (@Lupin1998) and Zicheng Liu (@pone7). We thank all contributors for Awesome-Long-Sequence!

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License

This project is released under the Apache 2.0 license.