Alvarez-Melis, D., Schiff, Y., and Mroueh, Y. Optimizing functionals on the space of probabilities with input convex neural networks. Transactions on Machine Learning Research, 2022.
Ansari, A. F., Ang, M. L., and Soh, H. Refining deep generative models via discriminator gradient flow. In International Conference on Learning Representations, 2021.
Arbel, M., Korba, A., Salim, A., and Gretton, A. Maximum mean discrepancy gradient flow. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché Buc, F., Fox, E., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, volume 32, pp. 6484-6494. Curran Associates, Inc., 2019.
Arjovsky, M., Chintala, S., and Bottou, L. Wasserstein generative adversarial networks. In Precup, D. and Teh, Y. W. (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 214-223. PMLR, August 2017.
Bengio, Y., Courville, A., and Vincent, P. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1798-1828, August 2013.
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A. signSGD: Compressed optimisation for non-convex problems. In Dy, J. and Krause, A. (eds.), Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pp. 560-569. PMLR, July 2018.
Brock, A., Donahue, J., and Simonyan, K. Large scale GAN training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2019.
Che, T., Zhang, R., Sohl-Dickstein, J., Larochelle, H., Paull, L., Cao, Y., and Bengio, Y. Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.-F., and Lin, H.-T. (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 12275-12287. Curran Associates, Inc., 2020.
Chu, C., Minami, K., and Fukumizu, K. The equivalence between Stein variational gradient descent and black-box variational inference. arXiv preprint arXiv:2004.01822, 2020.
Couairon, G., Verbeek, J., Schwenk, H., and Cord, M. DiffEdit: Diffusion-based semantic image editing with mask guidance. In International Conference on Learning Representations, 2023.
Dhariwal, P. and Nichol, A. Diffusion models beat GANs on image synthesis. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W. (eds.), Advances in Neural Information Processing Systems, volume 34, pp. 8780-8794. Curran Associates, Inc., 2021.
Duncan, A., Nusken, N., and Szpruch, L. On the geometry of Steinl'heurrrr variational gradient descent. Journal of Machine Learning Research, 24(56):1-39, 2023.
Durr, S., Mroueh, Y., Tu, Y., and Wang, S. Effective dynamics of generative adversarial networks. arXiv preprint arXiv:2212.04580, 2022.
Fallis, D. The epistemic threat of deepfakes. Philosophy & Technology, 34:623-643, 2021.
Fan, J., Zhang, Q., Taghvaei, A., and Chen, Y. Variational Wasserstein gradient flow. In Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., and Sabato, S. (eds.), Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 6185-6215. PMLR, July 2022.
Franceschi, J.-Y., De Bézenac, E., Ayed, I., Chen, M., Lamprier, S., and Gallinari, P. A neural tangent kernel perspective of GANs. In Chaudhuri, K., Jegelka, S., Song, L., Szepesváři, C., Niu, G., and Sabato, S. (eds.), Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 6660-6704. PMLR, July 2022.
Gao, Y., Jiao, Y., Wang, Y., Wang, Y., Yang, C., and Zhang, S. Deep generative learning via variational gradient flow. 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. 2093-2101. PMLR, June 2019.