[15] Elena Trunz, Michael Weinmann, Sebastian Merzbach, and Reinhard Klein. Efficient structuring of the latent space for controllable data reconstruction and compression. Graphics and Visual Computing, 7:200059, 2022.
[16] David Wipf and Srikantan Nagarajan. A new view of automatic relevance determination. Advances in neural information processing systems, 20, 2007.
[17] Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar. TANGOS: Regularizing tabular neural networks through gradient orthogonalization and specialization. In The Eleventh International Conference on Learning Representations, 2023.
[18] Maurice S Bartlett. The use of transformations. Biometrics. Journal of the International Biometric Society, 3(1):39-52, 1947.
[19] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[20] Yiping Sun, Zhaoyu Li, Jiadui Chen, Xuefeng Zhao, and Meng Tao. Variational autoencoder-based topological optimization of an anechoic coating: An efficient- and neural network-based design. Materials Today Communications, 32:103901, 2022.
[21] Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt. Topological autoencoders. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 7045-7054. PMLR, July 2020.
[22] Bin Dai and David Wipf. Diagnosing and enhancing VAE models. arXiv preprint arXiv:1903.05789, 2019.
[23] Carlos Lassance, Vincent Gripon, and Antonio Ortega. Representing deep neural networks: latent space geometries with graphs. Algorithms, 14(2):39, 2021.
[24] Michael Tipping. The relevance vector machine. Advances in neural information processing systems, 12, 1999.
[25] Christopher M Bishop and Michael Tipping. Variational relevance vector machines. arXiv preprint arXiv:1301.3838, 2013.
[26] P Thomas Fletcher, Conglin Lu, Stephen M Pizer, and Sarang Joshi. Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE transactions on medical imaging, 23(8):995-1005, 2004.
[27] Guy Bresler, Sung Min Park, and Madalina Persu. Sparse PCA from sparse linear regression. Advances in Neural Information Processing Systems, 31, 2018.
[28] Jonatan Henriquez and Werner Kristjanpoller. A combined Independent Component Analysis-Neural Network model for forecasting exchange rate variation. Applied Soft Computing, 83:105654, 2019.
[29] Rizwan A Choudrey. Variational Methods for Bayesian Independent Component Analysis. PhD thesis, University of Oxford Oxford, UK, 2002.
[30] Isadore Manuel Singer and Jack Thorpe. Lecture notes on elementary topology and geometry., 1967.
[31] Eike Cramer, Alexander Mitsos, Raul Tempone, and Manuel Dahmen. Principal component density estimation for scenario generation using normalizing flows. Data-Centric Engineering, 3, 2022.
[32] Edmond Cunningham, Adam D Cobb, and Susmit Jha. Principal component flows. In International Conference on Machine Learning, pages 4492-4519. PMLR, 2022.
[33] Rob Cornish, Anthony Caterini, George Deligiannidis, and Arnaud Doucet. Relaxing bijectivity constraints with continuously indexed normalising flows. In International Conference on Machine Learning, pages 2133-2143. PMLR, 2020.