DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks

Florian Eilers, Xiaoyi Jiang

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DOI: 10.1109/tpami.2026.3732508
Published: 2026
Container: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1109/tpami.2026.3732508,
  title = {DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks},
  author = {Florian Eilers and Xiaoyi Jiang},
  year = {2026},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  doi = {10.1109/tpami.2026.3732508},
  url = {https://doi.org/10.1109/tpami.2026.3732508}
}

RIS

TY  - JOUR
TI  - DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
AU  - Florian Eilers
AU  - Xiaoyi Jiang
PY  - 2026
JO  - IEEE Transactions on Pattern Analysis and Machine Intelligence
DO  - 10.1109/tpami.2026.3732508
UR  - https://doi.org/10.1109/tpami.2026.3732508
ER  -

APA

Eilers, F., & Jiang, X. (2026). DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2026.3732508

Source records

crossref · retrieved 2026-09-25T06:20:21.654Z