Toward Intrinsically Interpretable Machine Learning Applications for Optical Networks
In this paper, we review the recent advances in explainable machine learning (ML) techniques and shed light on how to design intrinsically interpretable ML solutions for optical networks by two preliminary explorations.
Authors
- Zuqing Zhu (ORCID: https://orcid.org/0000-0002-4251-788X)
- Hanyu Gao (ORCID: https://orcid.org/0000-0002-4112-6166)
- Zhengyong Liu (ORCID: https://orcid.org/0000-0002-0737-1782)
- Xiaoliang Chen (ORCID: https://orcid.org/0000-0002-7805-6237)
- Xiaokang Chen (ORCID: https://orcid.org/0009-0007-6272-6535)
- Zhenlin Ouyang
Institutions
- University of Science and Technology of China (CN)
- Sun Yat-sen University (CN)
Publication Details
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1109/acp/ipoc63121.2024.10809676
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00