[39] Henning Petzka, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. Relative flatness and generalization. Advances in Neural Information Processing Systems, 34: 18420-18432, 2021.
[40] Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie. Gradient starvation: A learning proclivity in neural networks. Advances in Neural Information Processing Systems, 34:1256-1272, 2021.
[41] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.
[42] Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein. Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability. Advances in neural information processing systems, 30, 2017.
[43] Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, et al. A unified game-theoretic interpretation of adversarial robustness. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
[44] Jie Ren, Zhanpeng Zhou, Qirui Chen, and Quanshi Zhang. Can we faithfully represent absence states to compute shapley values on a dnn? In The Eleventh International Conference on Learning Representations, 2022.
[45] Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, and Quanshi Zhang. Defining and quantifying the emergence of sparse concepts in dnns. In IEEE Conference on Computer Vision and Pattern Recognition, 2023.
[46] Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, and Quanshi Zhang. Bayesian neural networks avoid encoding complex and perturbation-sensitive concepts. Proceedings of the 40th International Conference on Machine Learning, pages 28889-28913, 2023.
[47] Qihan Ren, Jiayang Gao, Wen Shen, and Quanshi Zhang. Where we have arrived in proving the emergence of sparse symbolic concepts in ai models. arXiv preprint arXiv:2305.01939, 2023.
[48] Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra. Can contrastive learning avoid shortcut solutions? Advances in neural information processing systems, 34:4974-4986, 2021.
[49] Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Michael Poli, and Sangdoo Yun. Which shortcut cues will dnns choose? a study from the parameter-space perspective. arXiv preprint arXiv:2110.03095, 2021.
[50] Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli. The pitfalls of simplicity bias in neural networks. Advances in Neural Information Processing Systems, 33:9573-9585, 2020.
[51] Lloyd S Shapley. A value for n-person games. Contributions to the Theory of Games, 2(28): 307-317, 1953.
[52] Wen Shen, Qihan Ren, Dongrui Liu, and Quanshi Zhang. Interpreting representation quality of dnns for 3d point cloud processing. Advances in Neural Information Processing Systems, 34: 8857-8870, 2021.
[53] Thomas J Shuell. Cognitive conceptions of learning. Review of educational research, 56(4): 411-436, 1986.
[54] Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017.
[55] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.