[32] Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, and Noam Shazeer. Fast decoding in sequence models using discrete latent variables. In International Conference on Machine Learning, 2018.
[33] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Conference on Computer Vision and Pattern Recognition, 2019.
[34] Ilyes Khemakhem, Diederik Kingma, Ricardo Monti, and Aapo Hyvarinen. Variational autoencoders and nonlinear ICA: a unifying framework. In International Conference on Artificial Intelligence and Statistics, 2020.
[35] Patrick Kidger and Cristian Garcia. Equinox: neural networks in JAX via callable PyTrees and filtered transformations. Differentiable Programming Workshop at Neural Information Processing Systems, 2021.
[36] Hyunjik Kim and Andriy Mnih. Disentangling by factorising. In International Conference on Machine Learning, pages 2649-2658. PMLR, 2018.
[37] Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, and Bryon Aragam. Identifiability of deep generative models without auxiliary information. In Advances in Neural Information Processing Systems, 2022.
[38] David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, and Dylan Paiton. Towards nonlinear disentanglement in natural data with temporal sparse coding. In International Conference on Learning Representations, 2021.
[39] Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, and Ryuji Hamamoto. Decomposing normal and abnormal features of medical images for content-based image retrieval of glioma imaging. Medical Image Analysis, 74:102227, 2021.
[40] Alexander Kraskov, Harald Stögbauer, and Peter Grassberger. Estimating mutual information. Physical Review E, 69(6):066138, 2004.
[41] Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan. Variational inference of disentangled latent concepts from unlabeled observations. In International Conference on Learning Representations, 2018.
[42] Sebastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, and Simon Lacoste-Julien. Disentanglement via mechanism sparsity regularization: a new principle for nonlinear ICA. In Conference on Causal Learning and Reasoning, 2022.
[43] Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, and Bernhard Scholkopf. Structure by architecture: structured representations without regularization. In International Conference on Learning Representations, 2023.
[44] Piyawat Lertvittayakumjorn and Francesca Toni. Explanation-based human debugging of NLP models: a survey. Transactions of the Association for Computational Linguistics, 9:1508-1528, 2021.
[45] Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna Kumar Gyawali, and Linwei Wang. Progressive learning and disentanglement of hierarchical representations. In International Conference on Learning Representations, 2020.
[46] Dianbo Liu, Alex M Lamb, Kenji Kawaguchi, Anirudh Goyal ALIAS PARTH GOYAL, Chen Sun, Michael C Mozer, and Yoshua Bengio. Discrete-valued neural communication. In Advances in Neural Information Processing Systems, 2021.
[47] Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. Challenging common assumptions in the unsupervised learning of disentangled representations. In International Conference on Machine Learning, 2019.
[48] Francesco Locatello, Ben Poole, Gunnar Ratsch, Bernhard Scholkopf, Olivier Bachem, and Michael Tschannen. Weakly-supervised disentanglement without compromises. In International Conference on Machine Learning, 2020.