[42] Zongyi Li, Nikola Borislavov Kovachki, Chris Choy, Boyi Li, Jean Kossaifi, Shourya Prakash Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, et al. Geometry-informed neural operator for large-scale 3d pdes. arXiv preprint arXiv:2309.00583, 2023.
[43] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012.
[44] 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.
[45] Krishna Karthik Gadiraju, Bharathkumar Ramachandra, Zexi Chen, and Ranga Raju Vatsavai. Multimodal deep learning based crop classification using multispectral and multitemporal satellite imagery. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3234-3242, 2020.
[46] Yumin Liu, Auroop R Ganguly, and Jennifer Dy. Climate downscaling using yet: A deep convolutional network with skip connections and fusion. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3145-3153, 2020.
[47] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[48] Wenchong He, Zhe Jiang, Chengming Zhang, and Arpan Man Sainju. Curvanet: Geometric deep learning based on directional curvature for 3d shape analysis. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2214-2224, 2020.
[49] Neng Shi, Jiayi Xu, Skylar W Wurster, Hanqi Guo, Jonathan Woodring, Luke P Van Roekel, and Han-Wei Shen. Gnn-surrogate: A hierarchical and adaptive graph neural network for parameter space exploration of unstructured-mesh ocean simulations. IEEE Transactions on Visualization and Computer Graphics, 28(6):2301–2313, 2022.
[50] Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, and Guorong Wu. Dual-graph learning convolutional networks for interpretable alzheimer's disease diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 406-415. Springer, 2022.
[51] Wenchong He, Arpan Man Sainju, Zhe Jiang, and Da Yan. Deep neural network for 3d surface segmentation based on contour tree hierarchy. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pages 253-261. SIAM, 2021.
[52] Wenchong He, Arpan Man Sainju, Zhe Jiang, Da Yan, and Yang Zhou. Earth imagery segmentation on terrain surface with limited training labels: A semi-supervised approach based on physics-guided graph co-training. ACM Transactions on Intelligent Systems and Technology (TIST), 13(2):1-22, 2022.
[53] Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of the web conference 2020, pages 1082-1092, 2020.
[54] George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff. A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 2114-2124, 2021.
[55] Zhiruo Wang, Haoyu Dong, Ran Jia, Jia Li, Zhiyi Fu, Shi Han, and Dongmei Zhang. Tuta: Tree-based transformers for generally structured table pre-training. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1780-1790, 2021.
[56] Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan. Quadtree attention for vision transformers. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022.
[57] Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, and Samy Bengio. Learnable fourier features for multi-dimensional spatial positional encoding. Advances in Neural Information Processing Systems, 34:15816-15829, 2021.
[58] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pages 1050-1059. PMLR, 2016.
[59] Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton. Hyperparameter ensembles for robustness and uncertainty quantification. Advances in Neural Information Processing Systems, 33:6514-6527, 2020.
[60] Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, and Rose Yu. Quantifying uncertainty in deep spatiotemporal forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 1841-1851, 2021.
[61] Wenchong He and Zhe Jiang. A survey on uncertainty quantification methods for deep neural networks: An uncertainty source perspective. arXiv preprint arXiv:2302.13425, 2023.