[7] A. Dempster. Upper and lower probabilities induced by a multi- valued mapping. Annals of Mathematical Statistics, 38:325-339, 1967.
[8] J. F. Dipnall, R. Page, L. Du, M. Costa, R. A. Lyons, P. Cameron, R. de Steiger, R. Hau, A. Bucknill, A. Oppy, E. Edwards, D. Varma, M. C. Jung, and B. J. Gabbe. Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (praise) study protocol. PLOS ONE, 16(9):1-12, 09 2021. doi: 10.1371/journal.pone.0257361.
[9] L. Feng, H. Hajimirsadeghi, Y. Bengio, and M. O. Ahmed. Latent bottlenecked attentive neural processes. In The Eleventh International Conference on Learning Representations, 2023.
[10] A. Foong, W. Bruinsma, J. Gordon, Y. Dubois, J. Requeima, and R. Turner. Meta-learning stationary stochastic process prediction with convolutional neural processes. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 8284-8295. Curran Associates, Inc., 2020.
[11] Y. Gal and Z. Ghahramani. Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158, 2015.
[12] M. Gao, J. Jiang, G. Zou, V. John, and Z. Liu. RGB-d-based object recognition using multimodal convolutional neural networks: A survey. IEEE Access, 7:43110-43136, 2019. doi: 10.1109/ACCESS.2019.2907071.
[13] M. Garnelo, D. Rosenbaum, C. Maddison, T. Ramalho, D. Saxton, M. Shanahan, Y. W. Teh, D. Rezende, and S. M. A. Eslami. Conditional neural processes. In J. Dy and A. Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 1704–1713. PMLR, 10–15 Jul 2018.
[14] M. Garnelo, J. Schwarz, D. Rosenbaum, F. Viola, D. J. Rezende, S. Eslami, and Y. W. Teh. Neural processes. arXiv preprint arXiv:1807.01622, 2018.
[15] J. Gordon, W. P. Bruinsma, A. Y. K. Foong, J. Requeima, Y. Dubois, and R. E. Turner. Convolutional conditional neural processes. In International Conference on Learning Representations, 2020.
[16] C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. On calibration of modern neural networks. In D. Precup and Y. W. Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1321-1330. PMLR, 06-11 Aug 2017.
[17] Z. Han, C. Zhang, H. Fu, and J. T. Zhou. Trusted multi-view classification. In International Conference on Learning Representations, 2021.
[18] D. Hendrycks and T. Dietterich. Benchmarking neural network robustness to common corruptions and perturbations. Proceedings of the International Conference on Learning Representations, 2019.
[19] J. Hensman, A. Matthews, and Z. Ghahramani. Scalable Variational Gaussian Process Classification. In G. Lebanon and S. V. N. Vishwanathan, editors, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, pages 351–360, San Diego, California, USA, 09–12 May 2015. PMLR.
[20] P. Holderrieth, M. J. Hutchinson, and Y. W. Teh. Equivariant learning of stochastic fields: Gaussian processes and steerable conditional neural processes. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 4297-4307. PMLR, 18-24 Jul 2021.
[21] H. Hotelling. Relations between two sets of variates. Biometrika, 28(3/4):321-377, 1936. ISSN 00063444.
[22] S. Jha, D. Gong, H. Zhao, and Y. Lina. NPCL: Neural processes for uncertainty-aware continual learning. In Advances in Neural Information Processing Systems, 2023.