James Martens. New insights and perspectives on the natural gradient method. The Journal of Machine Learning Research, 21(1):5776-5851, 2020.
James Martens and Roger Grosse. Optimizing neural networks with kronecker-factored approximate curvature. In International conference on machine learning, pages 2408-2417, 2015.
Bruno Mlodozeniec, Matthias Reisser, and Christos Louizos. Hyperparameter optimization through neural network partitioning. arXiv preprint arXiv:2304.14766, 2023.
Behnam Neyshabur. Towards learning convolutions from scratch. Advances in Neural Information Processing Systems, 33:8078-8088, 2020.
Sebastian W Ober and Laurence Aitchison. Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes. In International Conference on Machine Learning, pages 8248-8259. PMLR, 2021.
Carl Rasmussen and Zoubin Ghahramani. Occam's razor. Advances in neural information processing systems, 13, 2000.
Carl Edward Rasmussen. Gaussian processes in machine learning. In Summer school on machine learning, pages 63-71. Springer, 2003.
David W Romero and Suhas Lohit. Learning equivariances and partial equivariances from data. arXiv preprint arXiv:2110.10211, 2021.
David W Romero and Neil Zeghidour. Dnarch: Learning convolutional neural architectures by backpropagation. arXiv preprint arXiv:2302.05400, 2023.
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton. Dynamic routing between capsules. Advances in neural information processing systems, 30, 2017.
Pola Schwöbel, Martin Jørgensen, Sebastian W Ober, and Mark van der Wilk. Last layer marginal likelihood for invariance learning. arXiv preprint arXiv:2106.07512, 2021.
Tycho FA van der Ouderaa and Mark van der Wilk. Learning invariant weights in neural networks. In Workshop in Uncertainty & Robustness in Deep Learning, ICML, 2021.
Tycho FA van der Ouderaa and Mark van der Wilk. Sparse convolutions on lie groups. In NeurIPS Workshop on Symmetry and Geometry in Neural Representations, pages 48-62. PMLR, 2023.
Tycho FA van der Ouderaa, David W Romero, and Mark van der Wilk. Relaxing equivariance constraints with non-stationary continuous filters. arXiv preprint arXiv:2204.07178, 2022.
Elise Van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, and Max Welling. Mdp homomorphic networks: Group symmetries in reinforcement learning. Advances in Neural Information Processing Systems, 33:4199-4210, 2020.
Mark van der Wilk, Matthias Bauer, ST John, and James Hensman. Learning invariances using the marginal likelihood. arXiv preprint arXiv:1808.05563, 2018.
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, pages 210-218. Springer, 2018.
Rui Wang, Robin Walters, and Rose Yu. Incorporating symmetry into deep dynamics models for improved generalization. arXiv preprint arXiv:2002.03061, 2020.
Rui Wang, Robin Walters, and Rose Yu. Approximately equivariant networks for imperfectly symmetric dynamics. arXiv preprint arXiv:2201.11969, 2022.
Maurice Weiler and Gabriele Cesa. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, 32, 2019.