[39] Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville. Film: Visual reasoning with a general conditioning layer. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 3942-3951. AAAI Press, 2018.
[40] Ali Rahimi and Ben Recht. Random Features for Large-Scale Kernel Machines. Advances in Neural Information Processing Systems, 2007.
[41] Matteo Ravasi and Ivan Vasconcelos. PyLops—A linear-operator Python library for scalable algebra and optimization. SoftwareX, 11:100361, 2020. ISSN 2352-7110. doi: https://doi.org/10.1016/j.softx.2019.100361. URL https://www.sciencedirect.com/science/article/pii/S2352711019301086.
[42] Nicolas Roux, Pierre-Antoine Manzagol, and Yoshua Bengio. Topmoumoute online natural gradient algorithm. Advances in neural information processing systems, 20, 2007.
[43] Youcef Saad and Martin H Schultz. Gmres: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on scientific and statistical computing, 7 (3):856-869, 1986.
[44] Yousef Saad. Iterative methods for sparse linear systems. SIAM, 2003.
[45] Ohad Shamir. A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate. arXiv preprint arXiv:1409.2848v5, 2015.
[46] Edward Snelson and Zoubin Ghahramani. Sparse gaussian processes using pseudo-inputs. Advances in neural information processing systems, 18, 2005.
[47] Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. Chainer: a next-generation open source framework for deep learning. In NeurIPS Workshop on Machine Learning Systems (LearningSys), volume 5, pages 1-6, 2015.
[48] Lloyd N. Trefethen and David Bau. Numerical Linear Algebra. SIAM, 1997.
[49] Ewout van den Berg and Michael P. Friedlander. Spot – A Linear-Operator Toolbox. SoftwareX, 2013. URL http://www.cs.ubc.ca/labs/scl/spot/.
[50] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antonio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17:261-272, 2020. doi: 10.1038/s41592-019-0686-2.
[51] Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q Weinberger, and Andrew Gordon Wilson. Exact gaussian processes on a million data points. Advances in neural information processing systems, 32, 2019.
[52] Andrew Wilson and Hannes Nickisch. Kernel interpolation for scalable structured gaussian processes (kiss-gp). In International conference on machine learning, pages 1775-1784. PMLR, 2015.
[53] Andrew G Wilson, Elad Gilboa, Arye Nehorai, and John P Cunningham. Fast kernel learning for multidimensional pattern extrapolation. Advances in neural information processing systems, 27, 2014.
[54] Max A Woodbury. Inverting modified matrices. Department of Statistics, Princeton University, 1950.
[55] Peng Xu, Bryan He, Christopher De Sa, Ioannis Mitlagkas, and Chris Re. Accelerated stochastic power iteration. In International Conference on Artificial Intelligence and Statistics, pages 58-67. PMLR, 2018.