[3] Rana Ali Amjad and Bernhard C Geiger. Learning representations for neural network-based classification using the information bottleneck principle. IEEE transactions on pattern analysis and machine intelligence, 42(9):2225-2239, 2019.
[4] Marco Ancona, Cengiz Oztireli, and Markus Gross. Explaining deep neural networks with a polynomial time algorithm for shapley value approximation. In International Conference on Machine Learning, pages 272-281. PMLR, 2019.
[5] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In International conference on machine learning, pages 233–242. PMLR, 2017.
[6] Arthur Asuncion and David Newman. Uci machine learning repository, 2007.
[7] Chuanchuan Chen, Dongrui Liu, Changqing Xu, and Trieu-Kien Truong. Saks: Sampling adaptive kernels from subspace for point cloud graph convolution. IEEE Transactions on Circuits and Systems for Video Technology, 2023.
[8] Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, and Quanshi Zhang. A game-theoretic taxonomy of visual concepts in dnns. arXiv preprint arXiv:2106.10938, 2021.
[9] Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, and Quanshi Zhang. A hypothesis for the aesthetic appreciation in neural networks. arXiv preprint arXiv:2108.02646, 2021.
[10] Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, and Xia Hu. A general taylor framework for unifying and revisiting attribution methods. arXiv preprint arXiv:2105.13841, 2021.
[11] Huiqi Deng, Qihan Ren, Xu Chen, Hao Zhang, Jie Ren, and Quanshi Zhang. Discovering and explaining the representation bottleneck of dnns. International Conference on Learning Representations, 2022.
[12] Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, and Quanshi Zhang. Understanding and unifying fourteen attribution methods with taylor interactions. arXiv preprint arXiv:2303.01506, 2023.
[13] Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. Sharp minima can generalize for deep nets. In International Conference on Machine Learning, pages 1019-1028. PMLR, 2017.
[14] Stanislav Fort, Paweł Krzysztof Nowak, Stanisław Jastrzebski, and Srini Narayanan. Stiffness: A new perspective on generalization in neural networks. arXiv preprint arXiv:1901.09491, 2019.
[15] Robert Geirhos, Jorn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. Shortcut learning in deep neural networks. Nature Machine Intelligence, 2(11):665-673, 2020.
[16] Michel Grabisch and Marc Roubens. An axiomatic approach to the concept of interaction among players in cooperative games. International Journal of game theory, 28(4):547-565, 1999.
[17] Moritz Hardt, Ben Recht, and Yoram Singer. Train faster, generalize better: Stability of stochastic gradient descent. In International conference on machine learning, pages 1225-1234. PMLR, 2016.
[18] John C Harsanyi. A simplified bargaining model for the n-person cooperative game. International Economic Review, 4(2):194-220, 1963.
[19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.