[HST19] A. Hyvarinen, H. Sasaki, and R. Turner. "Nonlinear ica using auxiliary variables and generalized contrastive learning". In: The 22nd International Conference on Artificial Intelligence and Statistics. PMLR. 2019 (cit. on p. 9).
[Khe+20] I. Khemakhem, D. Kingma, R. Monti, and A. Hyvarinen. "Variational autoencoders and nonlinear ica: a unifying framework". In: International Conference on Artificial Intelligence and Statistics. PMLR. 2020 (cit. on pp. 1, 9).
[Koj+22] T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa. Large language models are zero-shot reasoners. 2022. arXiv: 2205.11916 (cit. on p. 1).
[KJU22] M. Kwon, J. Jeong, and Y. Uh. "Diffusion models already have a semantic latent space". arXiv preprint arXiv:2210.10960 (2022) (cit. on p. 10).
[Liu+21] N. Liu, S. Li, Y. Du, J. Tenenbaum, and A. Torralba. "Learning to compose visual relations". Advances in Neural Information Processing Systems (2021) (cit. on pp. 7, 10).
[Liu+22] N. Liu, S. Li, Y. Du, A. Torralba, and J. B. Tenenbaum. Compositional visual generation with composable diffusion models. 2022. arXiv: 2206.01714 (cit. on pp. 10, 16).
[Luo22] C. Luo. Understanding diffusion models: a unified perspective. 2022. arXiv: 2208.11970 (cit. on p. 13).
[Mik+13] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. "Distributed representations of words and phrases and their compositionality". Advances in neural information processing systems (2013) (cit. on pp. 1, 9).
[MYZ13] T. Mikolov, W.-t. Yih, and G. Zweig. "Linguistic regularities in continuous space word representations". In: Proceedings of the 2013 conference of the north american chapter of the association for computational linguistics: Human language technologies. 2013 (cit. on p. 9).
[MM21] G. Mita, M. Filippone, and P Michiardi. "An identifiable double vae for disentangled representations". In: International Conference on Machine Learning. PMLR. 2021 (cit. on p. 1).
[NBP22] N. G. Nair, W. G. C. Bandara, and V. M. Patel. Unite and conquer: cross dataset multimodal synthesis using diffusion models. 2022. arXiv: 2212.00793 (cit. on p. 10).
[Par+23] Y.-H. Park, M. Kwon, J. Jo, and Y. Uh. "Unsupervised discovery of semantic latent directions in diffusion models". arXiv preprint arXiv:2302.12469 (2023) (cit. on p. 10).
[PSM14] J. Pennington, R. Socher, and C. D. Manning. “Glove: global vectors for word representation”. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014 (cit. on p. 9).
[Rad+21] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. "Learning transferable visual models from natural language supervision". In: International Conference on Machine Learning. PMLR. 2021 (cit. on p. 1).
[Ram+22] A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical text-conditional image generation with clip latents. 2022. arXiv: 2204.06125 (cit. on p. 4).
[Sah+22] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghase mipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, et al. "Photorealistic text-to-image diffusion models with deep language understanding". Advances in neural information processing systems (2022) (cit. on p. 4).
[SE19] Y. Song and S. Ermon. "Generative modeling by estimating gradients of the data distribution". Advances in Neural Information Processing Systems (2019) (cit. on p. 4).
[VK+21] J. Von Kugelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, and F. Locatello. "Self-supervised learning with data augmentations provably isolates content from style". Advances in neural information processing systems (2021) (cit. on p. 9).
[ZW20] D. Zhou and X.-X. Wei. "Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-vae". Advances in Neural Information Processing Systems (2020) (cit. on p. 1).
[Zim+21] R. S. Zimmermann, Y. Sharma, S. Schneider, M. Bethge, and W. Brendel. "Contrastive learning inverts the data generating process". In: International Conference on Machine Learning. PMLR. 2021 (cit. on p. 9).