Publisher
Springer Nature Switzerland
Reference24 articles.
1. Agafonov, A., Ponomarev, A.: RevelioNN: retrospective extraction of visual and logical insights for ontology-based interpretation of neural networks. In: 2023 34th Conference of Open Innovations Association (FRUCT), pp. 3–9. IEEE, November 2023. https://doi.org/10.23919/FRUCT60429.2023.10328156, https://ieeexplore.ieee.org/document/10328156/
2. Bellucci, M., Delestre, N., Malandain, N., Zanni-merk, C.: Ontologies to build a predictive architecture to classify and explain. In: DeepOntoNLP Workshop @ESWC 2022 (2022). https://hal.archives-ouvertes.fr/hal-03684275
3. Bourgeais, V., Zehraoui, F., Ben Hamdoune, M., Hanczar, B.: Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data. BMC Bioinform. 22, 1–24 (2021). https://doi.org/10.1186/s12859-021-04370-7, https://doi.org/10.1186/s12859-021-04370-7
4. Lecture Notes in Computer Science;G Bourguin,2021
5. Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021). https://doi.org/10.1613/JAIR.1.12228