LRBench++: A framework for effective learning rate tuning and benchmarking

If you find this tool useful, please cite the following paper:

@INPROCEEDINGS{lrbenchplusplus,
    author={Jin, Hongpeng and Wei, Wenqi and Wang, Xuyu and Zhang, Wenbin and Wu, Yanzhao},
    booktitle={2023 IEEE Fifth International Conference on Cognitive Machine Intelligence (CogMI)},
    title={Rethinking Learning Rate Tuning in the Era of Large Language Models},
    year={2023},
    volume={},
    number={},
    pages={},  
    doi={}
}

@article{lrbench-tist,
    author = {Wu, Yanzhao and Liu, Ling},
    title = {Selecting and Composing Learning Rate Policies for Deep Neural Networks},
    year = {2022},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    issn = {2157-6904},
    url = {https://doi.org/10.1145/3570508},
    doi = {10.1145/3570508},
    journal = {ACM Trans. Intell. Syst. Technol.},
    month = {11},
}

@INPROCEEDINGS{lrbench2019,
    author={Wu, Yanzhao and Liu, Ling and Bae, Juhyun and Chow, Ka-Ho and Iyengar, Arun and Pu, Calton and Wei, Wenqi and Yu, Lei and Zhang, Qi},
    booktitle={2019 IEEE International Conference on Big Data (Big Data)},
    title={Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks},
    year={2019},
    volume={},
    number={},
    pages={1971-1980},  
    doi={10.1109/BigData47090.2019.9006104}
}