[16] R. Harth. Understanding individual neurons of ResNet through improved compositional formulas. In Pattern Recognition and Artificial Intelligence, pages 283-294. Springer International Publishing, 2022. doi: 10.1007/978-3-031-09282-4_24.
[17] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778. IEEE, 2016.
[18] L. T. Hennigen, A. Williams, and R. Cotterell. Intrinsic probing through dimension selection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.emnlp-main.15.
[19] E. Hernandez, S. Schwettmann, D. Bau, T. Bagashvili, A. Torralba, and J. Andreas. Natural language descriptions of deep features. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=NudBMY-tzDr.
[20] G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4700-4708. IEEE, 2017.
[21] S. Jia, P. Lin, Z. Li, J. Zhang, and S. Liu. Visualizing surrogate decision trees of convolutional neural networks. Journal of Visualization, 23(1):141-156, nov 2019. doi: 10.1007/s12650-019-00607-z.
[22] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc., 2012. URL https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf.
[23] B. La Rosa, R. Capobianco, and D. Nardi. Explainable inference on sequential data via memory-tracking. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2006–2013. International Joint Conferences on Artificial Intelligence Organization, 7 2020. doi: 10.24963/ijcai.2020/278. URL https://doi.org/10.24963/ijcai.2020/278.
[24] B. La Rosa, R. Capobianco, and D. Nardi. A self-interpretable module for deep image classification on small data. Applied Intelligence, aug 2022. doi: 10.1007/s10489-022-03886-6.
[25] B. La Rosa, G. Blasilli, R. Bourqui, D. Auber, G. Santucci, R. Capobianco, E. Bertini, R. Giot, and M. Angelini. State of the art of visual analytics for eXplainable deep learning. Computer Graphics Forum, 42(1):319-355, feb 2023. doi: 10.1111/cgf.14733.
[26] A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
[27] S. M. Makinwa, B. La Rosa, and R. Capobianco. Detection accuracy for evaluating compositional explanations of units. In AIxIA 2021 - Advances in Artificial Intelligence, pages 550-563. Springer International Publishing, 2022. doi: 10.1007/978-3-031-08421-8_38.
[28] R. Massidda and D. Bacciu. Knowledge-driven interpretation of convolutional neural networks. In Machine Learning and Knowledge Discovery in Databases, pages 356-371. Springer International Publishing, 2023. doi: 10.1007/978-3-031-26387-3_22.
[29] A. Mordvintsev, N. Pezzotti, L. Schubert, and C. Olah. Differentiable image parameterizations. Distill, 3(7), jul 2018. doi: 10.23915/distill.00012.
[30] J. Mu and J. Andreas. Compositional explanations of neurons. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 17153-17163. Curran Associates, Inc., 2020. URL https://proceedings.neurips.cc/paper/2020/file/c74956ffb38ba48ed6ce977af6727275-Paper.pdf.