Code for [Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model] (https://iopscience.iop.org/article/10.1088/2632-2153/ace756; https://arxiv.org/abs/2303.12302).
Please note that the posted data files are shorter and contain fewer instances than the ones used to produce the research results, in order to comply with GitHub's file-size requirements. The full data files are available from the authors/software developers.
NASA_Open_Source_Agreement_ARC-18940-1_DVAE (https://github.com/nasa/dvae/blob/main/NASA_Open_Source_Agreement_ARC-18940-1_DVAE.pdf)
python=3.9.13
pytorch=1.13.1
numpy=1.21.5
scipy=1.9.1
scikit-learn=1.0.2
matplotlib=3.5.2
seaborn=0.11.2
kmodes=0.12.2
inflect=6.0.4
discrete_stochastic_training.py (-md validation -m boltzmann -e 400 ...)
discrete_stochastic_evaluation.py (-md validation -m boltzmann -e 400 ...)
The Jupyter notebook file dvae_example.ipynb contains guidance on how to train and evaluate our DVAE model for the purpose of anomaly detection in time-series data.
@article{Templin_2023,
title={Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model},
author={Templin, Thomas and Memarzadeh, Milad and Vinci, Walter and Lott, P. Aaron and Akbari Asanjan, Ata and Alexiades Armenakas, Anthony and Rieffel, Eleanor},
journal={Machine Learning: Science and Technology},
volume={4},
number={3},
pages={035018},
year={2023},
publisher={IOP Publishing}
}
@article{templin2023anomaly,
title={Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model},
author={Templin, Thomas and Memarzadeh, Milad and Vinci, Walter and Lott, P. Aaron and Akbari Asanjan, Ata and Alexiades Armenakas, Anthony and Rieffel, Eleanor},
journal={arXiv preprint arXiv:2303.12302},
year={2023}
}
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