Dwaracherla, V., Wen, Z., Osband, I., Lu, X., Asghari, S. M., and Van Roy, B. (2022). Ensembles for uncertainty estimation: Benefits of prior functions and bootstrapping. arXiv preprint arXiv:2206.03633.
Efron, B. and Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC press.
Gal, Y. and Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In International Conference on Machine Learning.
Gal, Y., Islam, R., and Ghahramani, Z. (2017). Deep Bayesian active learning with image data. In International Conference on Machine Learning, pages 1183-1192. PMLR.
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th international conference on artificial intelligence and statistics, pages 249-256.
Havasi, M., Jenatton, R., Fort, S., Liu, J. Z., Snoek, J., Lakshminarayanan, B., Dai, A. M., and Tran, D. (2020). Training independent subnetworks for robust prediction. CoRR, abs/2010.06610.
He, B., Lakshminarayanan, B., and Teh, Y. W. (2020). Bayesian deep ensembles via the neural tangent kernel. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., editors, Advances in Neural Information Processing Systems, volume 33, pages 1010-1022. Curran Associates, Inc.
Hendrycks, D. and Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations.
Hinton, G. E. and Van Camp, D. (1993). Keeping the neural networks simple by minimizing the description length of the weights. In Proceedings of the sixth annual conference on Computational learning theory, pages 5-13.
Hron, J., Matthews, A. G. d. G., and Ghahramani, Z. (2017). Variational Gaussian dropout is not Bayesian. arXiv preprint arXiv:1711.02989.
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G. (2021). What are Bayesian neural network posteriors really like? arXiv preprint arXiv:2104.14421.
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
Kendall, A. and Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems, volume 30.
Knothe, H. (1957). Contributions to the theory of convex bodies. Michigan Mathematical Journal, 4(1):39 - 52.
Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. Technical Report 0, University of Toronto, Toronto, Ontario.
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in Neural Information Processing Systems, pages 6405-6416.
Liu, J., Lin, Z., Padhy, S., Tran, D., Bedrax Weiss, T., and Lakshminarayanan, B. (2020). Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. Advances in Neural Information Processing Systems, 33:7498-7512.
Louizos, C. and Welling, M. (2017). Multiplicative normalizing flows for variational Bayesian neural networks. In International Conference on Machine Learning, pages 2218-2227. PMLR.
Lu, X. and Van Roy, B. (2017). Ensemble sampling. In Advances in Neural Information Processing Systems, pages 3260-3268.
MacKay, D. J. (1992). A practical Bayesian framework for backpropagation networks. *Neural computation*, 4(3):448-472.
Malinin, A. and Gales, M. (2018). Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, 31.