Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Ratsch, and Khan Mohammad Emtiyaz. Scalable marginal likelihood estimation for model selection in deep learning. In International Conference on Machine Learning, pages 4563-4573. PMLR, 2021.
Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Ratsch, and Mark van der Wilk. Invariance learning in deep neural networks with differentiable laplace approximations, 2022.
Alexander Immer, Tycho FA Van Der Ouderaa, Mark Van Der Wilk, Gunnar Ratsch, and Bernhard Schölkopf. Stochastic marginal likelihood gradients using neural tangent kernels. In International Conference on Machine Learning, pages 14333-14352. PMLR, 2023.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
David M Knigge, David W Romero, and Erik J Bekkers. Exploiting redundancy: Separable group convolutional networks on lie groups. In International Conference on Machine Learning, pages 11359-11386. PMLR, 2022.
Risi Kondor and Shubhendu Trivedi. On the generalization of equivariance and convolution in neural networks to the action of compact groups. In International Conference on Machine Learning, pages 2747-2755. PMLR, 2018.
Adam Kosiorek, Sara Sabour, Yee Whye Teh, and Geoffrey E Hinton. Stacked capsule autoencoders. Advances in neural information processing systems, 32, 2019.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
Yann LeCun. The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/, 1998.
Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 2, 1989.
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.
Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055, 2018a.
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. An intriguing failing of convolutional neural networks and the coordconv solution. arXiv preprint arXiv:1807.03247, 2018b.
Jonathan Lorraine, Paul Vicol, and David Duvenaud. Optimizing millions of hyperparameters by implicit differentiation. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1540-1552. PMLR, 26-28 Aug 2020.
Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.
David JC MacKay. Bayesian interpolation. Neural computation, 4(3):415-447, 1992.
David JC MacKay. Information theory, inference and learning algorithms. Cambridge university press, 2003.
David JC MacKay et al. Bayesian nonlinear modeling for the prediction competition. ASHRAE transactions, 100(2):1053-1062, 1994.
Kaitlin Maile, Dennis George Wilson, and Patrick Forre. Equivariance-aware architectural optimization of neural networks. In The Eleventh International Conference on Learning Representations, 2022.