Related papers:
Angelov, P., & Soares, E. (2020). Towards explainable deep neural networks (xDNN). Neural Networks, 130, 185-194.
Soares, E., Angelov, P., Costa, B., & Castro, M. (2019, July). Actively semi-supervised deep rule-based classifier applied to adverse driving scenarios. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Git:
https://github.com/Plamen-Eduardo/xDNN—Python
Soares, E. A., Angelov, P. P., Costa, B., Castro, M., Nageshrao, S., & Filev, D. (2020). Explaining deep learning models through rule-based approximation and visualization. IEEE Transactions on Fuzzy Systems.
Soares, E., Angelov, P., Filev, D., Costa, B., Castro, M., & Nageshrao, S. (2019, December). Explainable density-based approach for self-driving actions classification. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) (pp. 469-474). IEEE.
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