[7] Johann Brehmer and Kyle Cranmer. Flows for simultaneous manifold learning and density estimation. ArXiv, abs/2003.13913, 2020.
[8] Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur D. Szlam, and Pierre Vandergheynst. Geometric deep learning: Going beyond euclidean data. IEEE Signal Processing Magazine, 34:18-42, 2016.
[9] Roberto Camporesi. Harmonic analysis and propagators on homogeneous spaces. Physics Reports, 196: 1-134, 1990.
[10] Ricky T. Q. Chen and Yaron Lipman. Riemannian flow matching on general geometries. ArXiv, abs/2302.03660, 2023.
[11] Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Kristjanson Duvenaud. Neural ordinary differential equations. In Neural Information Processing Systems, 2018.
[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. A simple framework for contrastive learning of visual representations. ArXiv, abs/2002.05709, 2020.
[13] Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and T. Jaakkola. Diffdock: Diffusion steps, twists, and turns for molecular docking. ArXiv, abs/2210.01776, 2022.
[14] Victor D. Dorobantu, Charlotte Borcherds, and Yisong Yue. Conformal generative modeling on triangulated surfaces. ArXiv, abs/2303.10251, 2023.
[15] Luca Falorsi and Patrick Forr'e. Neural ordinary differential equations on manifolds. ArXiv, abs/2006.06663, 2020.
[16] H. D. Fegan. The heat equation on a compact lie group. Transactions of the American Mathematical Society, 246:339-357, 1978. ISSN 00029947. URL http://www.jstor.org/stable/1997977.
[17] Tomas Geffner, George Papamakarios, and Andriy Mnih. Score modeling for simulation-based inference. ArXiv, abs/2209.14249, 2022.
[18] Mevlana Gemici, Danilo Jimenez Rezende, and Shakir Mohamed. Normalizing flows on riemannian manifolds. ArXiv, abs/1611.02304, 2016.
[19] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
[20] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Kristjanson Duvenaud. Ffjord: Free-form continuous dynamics for scalable reversible generative models. ArXiv, abs/1810.01367, 2018.
[21] Alexander Grigor'yan. Spectral theory and geometry: Estimates of heat kernels on riemannian manifolds. 1999.
[22] Sigurdur Helgason. Differential geometry, lie groups, and symmetric spaces. 1978.
[23] Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv: Learning, 2016.
[24] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. ArXiv, abs/2006.11239, 2020.
[25] Chin-Wei Huang, Milad Aghajohari, A. Bose, P. Panangaden, and Aaron C. Courville. Riemannian diffusion models. ArXiv, abs/2208.07949, 2022.
[26] Michael F. Hutchinson. A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines. Communications in Statistics - Simulation and Computation, 18:1059-1076, 1989.
[27] Aapo Hyvarinen. Estimation of non-normalized statistical models by score matching. J. Mach. Learn. Res., 6:695-709, 2005.
[28] Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and T. Jaakkola. Torsional diffusion for molecular conformer generation. ArXiv, abs/2206.01729, 2022.
[29] Jurgen Jost. Riemannian geometry and geometric analysis. 1995.
[30] Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, and Christopher De Sa. Equivariant manifold flows. In Neural Information Processing Systems, 2021.