2023 Preprint Discrete Diffusion Language Modeling by Estimating the Ratios of the Data Distribution Aaron Lou, Chenlin Meng, and Stefano Ermon In Preprint, 2023 arXiv NeurIPS Scaling Riemannian Diffusion Models Aaron Lou, Minkai Xu, and Stefano Ermon In Neural Information Processing Systems, 2023 arXiv Code NeurIPS Riemannian Residual Neural Networks Isay Katsman, Eric Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, and Christopher De Sa In Neural Information Processing Systems, 2023 arXiv Reflected Diffusion Models Aaron Lou, and Stefano Ermon In International Conference on Machine Learning, 2023 arXiv Blog Code 2021 Equivariant Manifold Flows Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa In Neural Information Processing Systems, 2021 arXiv Code NeurIPS Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks Tolga Birdal, Aaron Lou, Leonidas J. Guibas, and Umut Simsekli In Neural Information Processing Systems, 2021 arXiv Code 2020 Neural Manifold Ordinary Differential Equations Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa In Neural Information Processing Systems, 2020 arXiv Code Differentiating through the Fréchet Mean Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, and Christopher De Sa In International Conference on Machine Learning, 2020 arXiv Code
