DMFAL: Deep Multi-Fidelity Active Learning for High-Dimensional Outputs

by Shibo Li, Zheng Wang, Mike Kirby and Shandian Zhe

This is the python implementation of the our papr [Deep Multi-Fidelity Active Learning for High-Dimensional Outputs]

System Requirement

See the docker file

We highly recommend to use Docker to freeze the running experiments. We attach our docker build file.

Run

see the scripts fold, it shows the running examples

License

DMFAL is released under the MIT License, please refer the LICENSE for details

Citation

Please cite our work if you would like to use the code

@misc{li2021deep,
      title={Deep Multi-Fidelity Active Learning of High-dimensional Outputs}, 
      author={Shibo Li and Zheng Wang and Robert M. Kirby and Shandian Zhe},
      year={2021},
      eprint={2012.00901},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contact

If you have any questions, please email me at shibo 'at' cs.utah.edu, or create an issue on github.