[49] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
[50] Sergio Naval Marimont and Giacomo Tarroni. Anomaly detection through latent space restoration using vector quantized variational autoencoders. In International Symposium on Biomedical Imaging, 2021.
[51] Brent Mittelstadt. Auditing for transparency in content personalization systems. International Journal of Communication, 10:12, 2016.
[52] Gemma Elyse Moran, Dhanya Sridhar, Yixin Wang, and David Blei. Identifiable deep generative models via sparse decoding. Transactions on Machine Learning Research, 2022.
[53] Weili Nie. High resolution disentanglement datasets, 2019. URL https://github.com/NVlabs/High-res-disentanglement-datasets.
[54] Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B Patel, and Anima Anandkumar. Semi-supervised StyleGAN for disentanglement learning. In International Conference on Machine Learning, 2020.
[55] Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. Neural discrete representation learning. In Advances in Neural Information Processing Systems, 2017.
[56] Fabian Pedregosa, Géral Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12(85):2825–2830, 2011.
[57] William Peebles, John Peebles, Jun-Yan Zhu, Alexei Efros, and Antonio Torralba. The Hessian penalty: A weak prior for unsupervised disentanglement. In European Conference on Computer Vision, 2020.
[58] Karl Ridgeway. A survey of inductive biases for factorial representation-learning. arXiv preprint arXiv:1612.05299, 2016.
[59] Karl Ridgeway and Michael C Mozer. Learning deep disentangled embeddings with the F-statistic loss. In Advances in Neural Information Processing Systems, 2018.
[60] Brian C Ross. Mutual information between discrete and continuous data sets. PLOS ONE, 9(2): 1-5, 2014.
[61] Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, and Diane Bouchacourt. Disentanglement of correlated factors via hausdorff factorized support. In International Conference on Learning Representations, 2022.
[62] Philipp Schmidt and Felix Biessmann. Quantifying interpretability and trust in machine learning systems. In AAAI Workshop on Network Interpretability for Deep Learning, 2019.
[63] Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, and Ben Poole. Weakly supervised disentanglement with guarantees. In International Conference on Learning Representations, 2020.
[64] Peter Sorrenson, Carsten Rother, and Ullrich Köthe. Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN). In International Conference on Learning Representations, 2020.
[65] Henning Sprekeler, Tiziano Zito, and Laurenz Wiskott. An extension of slow feature analysis for nonlinear blind source separation. Journal of Machine Learning Research, 15(1):921-947, 2014.
[66] Alexander Strehl and Joydeep Ghosh. Cluster ensembles—a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3(Dec):583-617, 2002.