[31] Peter Spirtes and Clark Glymour. An algorithm for fast recovery of sparse causal graphs. Social Science Computer Review, 9(1):62-72, 1991.
[32] Adam Thomas Clark, Hao Ye, Forest Isbell, Ethan R Doyle, Jane Cowles, G David Tilman, and George Sugihara. Spatial convergent cross mapping to detect causal relationships from short time series. *Ecology*, 96(5):1174–1181, 2015.
[33] XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan. Estimating divergence functionals and the likelihood ratio by convex risk minimization. IEEE Transactions on Information Theory, 56(11):5847-5861, 2010.
[34] Michael Gutmann and Aapo Hyvärinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 297–304. JMLR Workshop and Conference Proceedings, 2010.
[35] Aaron Van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv e-prints, pages arXiv-1807, 2018.
[36] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597-1607. PMLR, 2020.
[37] Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshit Kumar, and Steven Hoi. Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting. In International Conference on Learning Representations, 2021.
[38] Yeping Hu, Xiaogang Jia, Masayoshi Tomizuka, and Wei Zhan. Causal-based time series domain generalization for vehicle intention prediction. In NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and Applications, 2021.
[39] Maciej Koch-Janusz and Zohar Ringel. Mutual information, neural networks and the renormalization group. Nature Physics, 14(6):578-582, 2018.
[40] Ting-I Hsieh, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. One-shot object detection with co-attention and co-excitation. Advances in Neural Information Processing Systems, 32, 2019.
[41] Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, and Di Lin. Ranet: Region attention network for semantic segmentation. Advances in Neural Information Processing Systems, 33:13927-13938, 2020.
[42] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7132-7141, 2018.
[43] Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R Manmatha, et al. Resnest: Split-attention networks. arXiv preprint arXiv:2004.08955, 2020.
[44] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 30, 2017.
[45] Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. Non-local neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7794-7803, 2018.
[46] Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, and Hongfu Liu. Selfdoc: Self-supervised document representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5652-5660, 2021.
[47] Chun-Fu Richard Chen, Quanfu Fan, and Rameswar Panda. Crossvit: Cross-attention multiscale vision transformer for image classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 357–366, 2021.