[9] Charles A Hall and W Weston Meyer. Optimal error bounds for cubic spline interpolation. Journal of Approximation Theory, 16(2):105-122, 1976.
[10] 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.
[11] Michael T Heath. Scientific Computing: An Introductory Survey, Revised Second Edition. Society for Industrial and Applied Mathematics, 2018.
[12] Sepp Hochreiter and Jürgen Schmidhuber. Long Short-term Memory. Neural Computation, 9(8):1735-1780, 1997.
[13] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700-4708, 2017.
[14] Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, and Yee Whye Teh. Attentive Neural Processes. In International Conference on Learning Representations, 2019.
[15] Diederik P Kingma and Max Welling. Auto-Encoding Variational Bayes. In International Conference on Learning Representations, 2014.
[16] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[17] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6):84-90, 2017.
[18] Byung-Jun Lee, Seunghoon Hong, and Kee-Eung Kim. Residual Neural Processes. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 4545-4552, 2020.
[19] Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, and Yee Whye Teh. Bootstrapping neural processes. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 6606-6615. Curran Associates, Inc., 2020.
[20] GR3543275 Liu. An Overview on Meshfree Methods: For Computational Solid Mechanics. International Journal of Computational Methods, 13(05):1630001, 2016.
[21] Tung Nguyen and Aditya Grover. Transformer Neural Processes: Uncertainty-aware Meta Learning via Sequence Modeling. arXiv preprint arXiv:2207.04179, 2022.
[22] Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. arXiv preprint arXiv:1905.10437, 2019.
[23] Ken Perlin. An Image Synthesizer. ACM Siggraph Computer Graphics, 19(3):287-296, 1985.
[24] Michael JD Powell. Radial Basis Functions for Multivariable Interpolation: A Review. Algorithms for Approximation, 1987.
[25] Andrea Sciacchitano, Douglas R Neal, Barton L Smith, Scott O Warner, Pavlos P Vlachos, Bernhard Wieneke, and Fulvio Scarano. Collaborative framework for piv uncertainty quantification: Comparative assessment of methods. Measurement Science and Technology, 26(7):074004, 2015.
[26] Donald Shepard. A Two-dimensional Interpolation Function for Irregularly-spaced Data. In Proceedings of the 23rd ACM National Conference, pages 517-524, 1968.
[27] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998-6008, 2017.