# Variational-HyperAdam

**Repository Path**: frontxiang/Variational-HyperAdam

## Basic Information

- **Project Name**: Variational-HyperAdam
- **Description**: No description available
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-06-04
- **Last Updated**: 2024-06-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Variational-HyperAdam

This is a PyTorch implementation of the Variational-HyperAdam algorithm from our TPAMI paper:

**Title**: Variational-HyperAdam: A Meta-learning Appproach to Network Training

**Authors**: Shipeng Wang, Yan Yang, Jian Sun, Zongben Xu

**Email**: wangshipeng8128@stu.xjtu.edu.cn; wangshipeng8128@gmail.com

**Institution**: School of Mathematics and Statistics, Xi'an Jiaotong University

**Link**: https://ieeexplore.ieee.org/document/9361276

Usage
-
To replicate the experiments,run from terminal:
```
cd HyperAdam
sh batch_process.sh
```


**Requirement**: PyTorch >= 1.0, Python 3.7


Citation
-
If the code is useful in your research, please cite ourpaper:
```
@ARTICLE{vrhyperadam2021wang,
  author={S. {Wang} and Y. {Yang} and J. {Sun} and Z. {Xu}},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Variational HyperAdam: A Meta-learning Approach to Network Training}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3061581}
  }
```