[30] Ali Razavi, Aïron van den Oord, Ben Poole, and Oriol Vinyals. Preventing posterior collapse with delta-vaes. In 7th International Conference on Learning Representations, (ICLR) 2019, New Orleans, LA, USA, May 6-9, 2019, 2019.
[31] James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi. Don't blame the elbo! a linear vae perspective on posterior collapse. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019.
[32] Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, and Patrick van der Smagt. Learning hierarchical priors in vaes. In Advances in Neural Information Processing Systems, volume 32, 2019.
[33] Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, and Lars Maaløe. Hierarchical VAEs know what they don't know. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning (ICML), volume 139 of Proceedings of Machine Learning Research (PMLR), pages 4117-4128, 18-24 Jul 2021.
[34] Adji B. Dieng, Yoon Kim, Alexander M. Rush, and David M. Blei. Avoiding latent variable collapse with generative skip models. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, pages 2397–2405, 2019.
[35] Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. Improved variational inference with inverse autoregressive flow. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems (NeurIPS), volume 29, 2016.
[36] Vaden Masrani, Tuan Anh Le, and Frank Wood. The thermodynamic variational objective. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019.
[37] Ali Razavi, Aaron van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. In Advances in Neural Information Processing Systems, volume 32, 2019.
[38] Arash Vahdat, Karsten Kreis, and Jan Kautz. Score-based generative modeling in latent space. In Advances in Neural Information Processing Systems, volume 34, pages 11287-11302, 2021.
[39] Chris Cremer, Xuechen Li, and David Duvenaud. Inference suboptimality in variational autoencoders. In International Conference on Machine Learning (ICML), pages 1078-1086. PMLR, 2018.
[40] Philip Bachman, Ouais Alsharif, and Doina Precup. Learning with pseudo-ensembles. In Advances in Neural Information Processing Systems (NeurIPS), volume 27, 2014.
[41] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In Advances in Neural Information Processing Systems (NeurIPS), volume 29, 2016.
[42] Yuri Burda, Roger B Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. In Proceedings of the International Conference on Learning Representations (ICLR), 2016.
[43] Tamara Broderick, Michael I Jordan, and Jim Pitman. Beta processes, stick-breaking and power laws. Bayesian Analysis, 7(2):439-476, 2012.
[44] Chris J Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712, 2016.
[45] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
[46] Mingzhang Yin and Mingyuan Zhou. Semi-implicit variational inference. In International Conference on Machine Learning (ICML), pages 5660–5669. PMLR, 2018.
[47] Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, volume 86 (11), page 2278-2324, 2014.
[48] Brenden M. Lake, Russ R. Salakhutdinov, and Josh Tenenbaum. One-shot learning by inverting a compositional causal process. In Advances in Neural Information Processing Systems (NeurIPS), 2013.
[49] Benjamin Marlin, Kevin Swersky, Bo Chen, and Nando Freitas. Inductive principles for restricted boltzmann machine learning. In Yee Whye Teh and Mike Titterington, editors, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), volume 9, pages 509-516. PMLR, 13-15 May 2010.