Kang, M., Shin, J., and Park, J. StudioGAN: A taxonomy and benchmark of GANs for image synthesis. arXiv preprint arXiv:2206.09479, 2022.
Karras, T., Aittala, M., Aila, T., and Laine, S. Elucidating the design space of diffusion-based generative models. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 26565-26577. Curran Associates, Inc., 2022.
Kingma, D. P. and Ba, J. Adam: A method for stochastic optimization. In International Conference on Learning Representations, 2015.
Klebaner, F. C. Introduction to Stochastic Calculus with Applications. Imperial College Press, 3rd edition, 2012.
Kloeden, P. E. and Platen, E. Introduction to Stochastic Time Discrete Approximation, pp. 305-337. Applications of Mathematics. Springer Berlin Heidelberg, Berlin - Heidelberg, Germany, 1992.
Krizhevsky, A. Learning multiple layers of features from tiny images. Technical report, University of Toronto, April 2009.
Kullback, S. and Leibler, R. A. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79-86, 1951.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
Lesniak, D., Sieradzki, I., and Podolak, I. Distribution-interpolation trade off in generative models. In International Conference on Learning Representations, 2018.
Li, X., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. B. Diffusion-LM improves controllable text generation. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A. (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 4328-4343. Curran Associates, Inc., 2022.
Lim, J. H. and Ye, J. C. Geometric GAN. arXiv preprint arXiv:1705.02894, 2017.
Liu, B., Zhu, Y., Song, K., and Elgammal, A. Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis. In International Conference on Learning Representations, 2021.
Liu, C., Zhu, L., and Belkin, M. On the linearity of large non-linear models: when and why the tangent kernel is constant. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.-F., and Lin, H.-T. (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 15954-15964. Curran Associates, Inc., 2020.
Liu, L., Lu, Y., Yang, M., Qu, Q., Zhu, J., and Li, H. Generative adversarial network for abstractive text summarization. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, AAAI'18, pp. 8109-8110. AAAI Press, 2018.
Liu, Q. Stein variational gradient descent as gradient flow. In Guyon, I., von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. V. N., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 30, pp. 3118-3126. Curran Associates, Inc., 2017.
Liu, Q. and Wang, D. Stein variational gradient descent: A general purpose Bayesian inference algorithm. In Lee, D. D., Sugiyama, M., von Luxburg, U., Guyon, I., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 29, pp. 2378-2386. Curran Associates, Inc., 2016.
Liu, Z., Luo, P., Wang, X., and Tang, X. Deep learning face attributes in the wild. In IEEE International Conference on Computer Vision (ICCV), pp. 3730-3738, December 2015.
Liutkus, A., Simsekli, U., Majewski, S., Durmus, A., and Stöter, F.-R. Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions. In Chaudhuri, K. and Salakhutdinov, R. (eds.), Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 4104-4113. PMLR, June 2019.