Glaser, P., Arbel, M., and Gretton, A. KALE flow: A relaxed KL gradient flow for probabilities with disjoint support. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W. (eds.), Advances in Neural Information Processing Systems, volume 34, pp. 8018-8031. Curran Associates, Inc., 2021.
Goodfellow, I. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160, 2016.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., and Weinberger, K. Q. (eds.), Advances in Neural Information Processing Systems, volume 27, pp. 2672-2680. Curran Associates, Inc., 2014.
Gretton, A., Borgwardt, K. M., Rasch, M. J., Scholkopf, B., and Smola, A. A kernel two-sample test. Journal of Machine Learning Research, 13(25):723-773, 2012.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. Improved training of Wasserstein GANs. 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. 5769-5779. Curran Associates, Inc., 2017.
Hendrycks, D. and Gimpel, K. Gaussian error linear units (GeLUs). arXiv preprint arXiv:1606.08415, 2016.
Heng, A., Ansari, A. F., and Soh, H. Deep generative Wasserstein gradient flows, 2023. URL https://openreview.net/forum?id=zjSeBTEdXp1.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. 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. 6629-6640. Curran Associates, Inc., 2017.
Ho, J., Chan, W., Sahara, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., and Salimans, T. Imagen Video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022.
Hsieh, Y.-P., Mertikopoulos, P., and Cevher, V. The limits of min-max optimization algorithms: Convergence to spurious non-critical sets. In Meila, M. and Zhang, T. (eds.), Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 4337-4348. PMLR, July 2021.
Hyvarinen, A. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6(24):695-709, 2005.
Ioffe, S. and Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Bach, F. and Blei, D. (eds.), Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pp. 448-456, Lille, France, July 2015. PMLR.
Jacot, A., Gabriel, F., and Hongler, C. Neural tangent kernel: Convergence and generalization in neural networks. In Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 31, pp. 8580-8589. Curran Associates, Inc., 2018.
Jelassi, S., Dobre, D., Mensch, A., Li, Y., and Gidel, G. Dissecting adaptive methods in GANs. arXiv preprint arXiv:2210.04319, 2022.
Jolicoeur-Martineau, A., Piche-Taillefer, R., Mitliagkas, I., and Tachet des Combes, R. Adversarial score matching and improved sampling for image generation. In International Conference on Learning Representations, 2021.
Jordan, R., Kinderlehrer, D., and Otto, F. The variational formulation of the Fokker-Planck equation. SIAM Journal on Mathematical Analysis, 29(1):1-17, 1998.