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Results for Interpretability

Publications & Outputs

  1. Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions

    Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., Jiang, R., Khosravi, H., Lecue, F., Malgieri, G., Páez, A., Samek, W., Schneider, J., Speith, T. & Stumpf, S., 30/06/2024, In: Information Fusion. 106, 102301.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  2. Detecting and Learning from Unknown by Extremely Weak Supervision: eXploratory Classifier (xClass)

    Angelov, P. & Almeida Soares, E., 30/11/2021, In: Neural Computing and Applications. 33, 22, p. 15145-15157 13 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  3. Towards explainable deep neural networks (xDNN)

    Angelov, P. & Soares, E., 1/10/2020, In: Neural Networks. 130, p. 185-194 10 p.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review

  4. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

    Huang, X., Kroening, D., Ruan, W., Sharp, J., Sun, Y., Thamo, E., Wu, M. & Yi, X., 1/08/2020, In: Computer Science Review. 37, 35 p., 100270.

    Research output: Contribution to Journal/Magazine › Journal article › peer-review