M. Cuturi, O. Teboul, and J.-P. Vert. Differentiable Ranks and Sorting using Optimal Transport, Nov. 2019. arXiv:1905.11885 [cs, stat].
R. De la Cruz-Mesía, F. A. Quintana, and P. Müller. Semiparametric Bayesian Classification with longitudinal Markers. Journal of the Royal Statistical Society Series C: Applied Statistics, 56(2): 119-137, Mar. 2007. ISSN 0035-9254. doi: 10.1111/j.1467-9876.2007.00569.x.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009.
N. Dilokthanakul, P. A. M. Mediano, M. Garnelo, M. C. H. Lee, H. Salimbeni, K. Arulkumaran, and M. Shanahan. Deep unsupervised clustering with Gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648, 2016.
R. A. Fisher. The logic of inductive inference. Journal of the royal statistical society, 98(1):39-82, 1935. Publisher: JSTOR.
A. Fog. Calculation methods for Wallenius' noncentral hypergeometric distribution. Communications in Statistics—Simulation and Computation® 37(2):258-273, 2008. Publisher: Taylor & Francis.
M. W. Gondal, M. Wuthrich, D. Miladinovic, F. Locatello, M. Breidt, V. Volchkov, J. Akpo, O. Bachem, B. Scholkopf, and S. Bauer. On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019.
R. L. Graham, D. E. Knuth, O. Patashnik, S. L.-C. i. Physics, and undefined 1989. Concrete mathematics: a foundation for computer science. aip.scitation.org, 3(5):165, 1989. doi: 10.1063/1.4822863. Publisher: AIP Publishing.
A. Grover, E. Wang, A. Zweig, and S. Ermon. Stochastic Optimization of Sorting Networks via Continuous Relaxations. In International Conference on Learning Representations, 2019.
J. A. Hartigan. Partition models. Communications in Statistics - Theory and Methods, 19(8): 2745-2756, 1990. doi: 10.1080/03610929008830345. Publisher: Taylor & Francis.
K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition, Dec. 2015.
I. Higgins, L. Matthew, A. Pal, C. Burgess, and X. Glorot. beta-vae: Learning basic visual concepts with a constrained variational framework. 2016.
H. Hosoya. A simple probabilistic deep generative model for learning generalizable disentangled representations from grouped data. CoRR, abs/1809.0, 2018.
E. Jang, S. Gu, and B. Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
Z. Jiang, Y. Zheng, H. Tan, B. Tang, and H. Zhou. Variational deep embedding: An unsupervised and generative approach to clustering. arXiv preprint arXiv:1611.05148, 2016.
M. E. Johnson. Multivariate statistical simulation: A guide to selecting and generating continuous multivariate distributions, volume 192. John Wiley & Sons, 1987.
D. P. Kingma and M. Welling. Auto-Encoding Variational Bayes. In International Conference on Learning Representations, 2014.
V. Kurin, A. D. Palma, I. Kostrikov, S. Whiteson, and M. P. Kumar. In Defense of the Unitary Scalarization for Deep Multi-Task Learning. CoRR, abs/2201.04122, 2022.
Y. LeCun, C. Cortes, and C. J. Burges. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, volume 86, pages 2278-2324, 1998. Issue: 11.
C. J. Lee and H. Sang. Why the Rich Get Richer? On the Balancedness of Random Partition Models. arXiv preprint arXiv:2201.12697, 2022.