[39] A. Martins and R. Astudillo. From softmax to sparsemax: A sparse model of attention and multi-label classification. In M. F. Balcan and K. Q. Weinberger, editors, Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pages 1614–1623, New York, New York, USA, 20–22 Jun 2016. PMLR.
[40] L. Meronen, C. Irwanto, and A. Solin. Stationary activations for uncertainty calibration in deep learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 2338-2350. Curran Associates, Inc., 2020.
[41] L. Meronen, M. Trapp, and A. Solin. Periodic activation functions induce stationarity. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 1673-1685. Curran Associates, Inc., 2021.
[42] D. Milios, R. Camoriano, P. Michiardi, L. Rosasco, and M. Filippone. Dirichlet-based gaussian processes for large-scale calibrated classification. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[43] M. Minderer, J. Djolonga, R. Romijnders, F. A. Hubis, X. Zhai, N. Houlsby, D. Tran, and M. Lucic. Revisiting the calibration of modern neural networks. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[44] K. P. Murphy. Probabilistic machine learning: an introduction. MIT press, 2022.
[45] T. Nair, D. Precup, D. L. Arnold, and T. Arbel. Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Medical Image Analysis, 59:101557, 2020. ISSN 1361-8415. doi: https://doi.org/10.1016/j.media.2019.101557.
[46] Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
[47] T. Nguyen and A. Grover. Transformer neural processes: Uncertainty-aware meta learning via sequence modeling. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 16569-16594. PMLR, 17-23 Jul 2022.
[48] J. Requeima, J. Gordon, J. Bronskill, S. Nowozin, and R. E. Turner. Fast and flexible multi-task classification using conditional neural adaptive processes. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[49] A. G. Roy, S. Conjeti, N. Navab, and C. Wachinger. Bayesian quicknat: Model uncertainty in deep whole-brain segmentation for structure-wise quality control. NeuroImage, 195:11-22, 2019. ISSN 1053-8119. doi: https://doi.org/10.1016/j.neuroimage.2019.03.042.
[50] M. Sensoy, L. Kaplan, and M. Kandemir. Evidential deep learning to quantify classification uncertainty. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[51] G. Singh, J. Yoon, Y. Son, and S. Ahn. Sequential neural processes. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
[52] C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid. Videobert: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019.
[53] A. Suresh and S. Srinivasan. Improved attentive neural processes. In Fourth Workshop on Bayesian Deep Learning at the Conference on Neural Information Processing Systems, volume 14, page 17, 2019.