[10] Nicki S Detlefsen, Martin Jørgensen, and Søren Hauberg. Reliable training and estimation of variance networks. In Advances in Neural Information Processing Systems, 2019.
[11] Nicki S. Detlefsen, Alison Pouplin, Cilie W. Feldager, Cong Geng, Dimitris Kalatzis, Helene Hauschultz, Miguel González Duque, Frederik Warburg, Marco Miani, and Søren Hauberg. StochMan, 2021. URL https://github.com/MachineLearningLifeScience/stochman/.
[12] F Dan Foresee and Martin T Hagan. Gauss-Newton approximation to Bayesian learning. In Proceedings of International Conference on Neural Networks (ICNN'97), volume 3, pages 1930–1935. IEEE, 1997.
[13] 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.
[14] Samuel Gershman and Noah Goodman. Amortized inference in probabilistic reasoning. In Proceedings of the annual meeting of the cognitive science society, volume 36, 2014.
[15] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1735-1742. IEEE, 2006.
[16] 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.
[17] Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, October 2007.
[18] Alexander Immer, Maciej Korzepa, and Matthias Bauer. Improving predictions of Bayesian neural nets via local linearization. In International Conference on Artificial Intelligence and Statistics, pages 703-711. PMLR, 2021.
[19] Diederik P Kingma and Max Welling. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013.
[20] J. Krause, M. Stark, J. Deng, and L. Fei-Fei. 3d object representations for fine-grained categorization. In 2013 IEEE International Conference on Computer Vision Workshops, pages 554–561, 2013.
[21] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009.
[22] Frederik Kunstner, Philipp Hennig, and Lukas Balles. Limitations of the empirical fisher approximation for natural gradient descent. Advances in neural information processing systems, 32, 2019.
[23] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in Neural Information Processing Systems, 30, 2017.
[24] Pierre Simon Laplace. Mémoire sur la probabilité des causes par les événements. Mémoire de l'Académie Royale des Sciences, 1774.
[25] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in Neural Information Processing Systems, 1989.
[26] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[27] David J. C. MacKay. A practical Bayesian framework for backpropagation networks. Neural Computation, 4(3):448-472, 1992.