[AH19] C. Allen and T. Hospedales. "Analogies explained: towards understanding word embeddings". In: International Conference on Machine Learning. PMLR. 2019 (cit. on p. 9).
[Ano23] Anonymous. "Reduce, reuse, recycle: compositional generation with energy-based diffusion models and MCMC". In: Submitted to The Eleventh International Conference on Learning Representations. under review. 2023 (cit. on p. 10).
[Aro+15] S. Arora, Y. Li, Y. Liang, T. Ma, and A. Risteski. A latent variable model approach to pmi-based word embeddings. 2015. arXiv: 1502.03520 (cit. on p. 9).
[Bol+16] T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai. "Man is to computer programmer as woman is to homemaker? debiasing word embeddings". Advances in neural information processing systems (2016) (cit. on p. 1).
[Bom+21] R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselat, E. Brunskill, et al. On the opportunities and risks of foundation models. 2021. arXiv: 2108.07258 (cit. on p. 1).
[Bro+20] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. "Language models are few-shot learners". Advances in neural information processing systems (2020) (cit. on p. 1).
[Cou+22] G. Couairon, J. Verbeek, H. Schwenk, and M. Cord. "Diffedit: diffusion-based semantic image editing with mask guidance". arXiv preprint arXiv:2210.11427 (2022) (cit. on p. 10).
[DLM20] Y. Du, S. Li, and I. Mordatch. "Compositional visual generation with energy based models". Advances in Neural Information Processing Systems (2020) (cit. on p. 10).
[Du+21] Y. Du, S. Li, Y. Sharma, J. Tenenbaum, and I. Mordatch. "Unsupervised learning of compositional energy concepts". Advances in Neural Information Processing Systems (2021) (cit. on pp. 7, 10).
[Eas+22] C. Eastwood, A. L. Nicolicioiu, J. von Kugelgen, A. Kekić, F. Trauble, A. Dittadi, and B. Schölkopf. "Dci-es: an extended disentanglement framework with connections to identifiability". arXiv preprint arXiv:2210.00364 (2022) (cit. on p. 9).
[Elh+22] N. Elhage, T. Hume, C. Olsson, N. Schiefer, T. Henighan, S. Kravec, Z. Hatfield-Dodds, R. Lasenby, D. Drain, C. Chen, et al. "Toy models of superposition". arXiv preprint arXiv:2209.10652 (2022) (cit. on p. 1).
[GAM17] A. Gittens, D. Achlioptas, and M. W. Mahoney. "Skip-gram- zipf+ uniform= vector additivity". In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017 (cit. on p. 9).
[GL14] Y. Goldberg and O. Levy. Word2vec explained: deriving mikolov et al.'s negative-sampling word-embedding method. 2014. arXiv: 1402.3722 (cit. on p. 9).
[GG19] H. Gonen and Y. Goldberg. Lipstick on a pig: debiasing methods cover up systematic gender biases in word embeddings but do not remove them. 2019. arXiv: 1903.03862 (cit. on p. 1).
[Haa+23] R. Haas, I. Huberman-Spiegelglas, R. Mulayoff, and T. Michaeli. "Discovering interpretable directions in the semantic latent space of diffusion models". arXiv preprint arXiv:2303.11073 (2023) (cit. on p. 10).
[Hig+18] I. Higgins, D. Amos, D. Pfau, S. Racaniere, L. Matthew, D. Rezende, and A. Lerchner. "Towards a definition of disentangled representations". arXiv preprint arXiv:1812.02230 (2018) (cit. on p. 9).
[HJA20] J. Ho, A. Jain, and P. Abbeel. "Denoising diffusion probabilistic models". Advances in Neural Information Processing Systems (2020) (cit. on pp. 4, 13).
[HD05] A. Hyvärinen and P. Dayan. "Estimation of non-normalized statistical models by score matching." Journal of Machine Learning Research 4 (2005) (cit. on p. 4).
[HM16] A. Hyvarinen and H. Morioka. "Unsupervised feature extraction by time-contrastive learning and nonlinear ica". Advances in neural information processing systems (2016) (cit. on p. 9).
[HM17] A. Hyvarinen and H. Morioka. "Nonlinear ica of temporally dependent stationary sources". In: Artificial Intelligence and Statistics. PMLR. 2017 (cit. on p. 9).