tf-titans 0.1.2

A custom implementation of titans architecture.

Custom Implementation of Titans Architecture in TensorFlow

Overview

This repository provides a custom implementation of the Titans architecture using TensorFlow. The aim is to harness state-of-the-art neural network design principles to develop scalable and efficient deep learning models.

Description

The repository presents an implementation based on the Titans architecture described in the paper "Titans: Learning to Memorize at Test Time". Please note that only "Memory as a Context" has been implemented, and some variations may exist compared to the paper.

Getting Started

Prerequisites

• Python 3.7 or later
• TensorFlow 2.x

Installation

pip install tf-titans

Usage

Refer to the example file to get started. It is recommended to use the custom training function for models that incorporate memory.

Contributing

Contributions are welcome. Please feel free to submit issues and pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for further details.

Contact

For inquiries or further discussion, please contact [e-mail].

Citations

@inproceedings{Behrouz2024TitansLT,
    title   = {Titans: Learning to Memorize at Test Time},
    author  = {Ali Behrouz and Peilin Zhong and Vahab S. Mirrokni},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:275212078}
}