[1] ADAMU, I. Numerical Approximation of SDEs and Stochastic Swift-Hohenberg Equation (PhD Thesis, Heriot-Watt University), 2011.
[2] BAO, F., LI, C., ZHU, J., AND ZHANG, B. Analytic-DPM: An Analytic Estimate Of The Optimal Reverse Variance In Diffusion Probabilistic Models. ICLR (2022).
[3] CHEN, N., ZHANG, Y., ZEN, H., WEISS, R. J., NOROUZI, M., AND CHAN, W. WaveGrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713 (2020).
[4] CHEN, N., ZHANG, Y., ZEN, H., WEISS, R. J., NOROUZI, M., DEHAK, N., AND CHAN, W. WaveGrad 2: Iterative refinement for text-to-speech synthesis. arXiv preprint arXiv:2106.09660 (2021).
[5] CHEN, S., CHEWI, S., LI, J., LI, Y., SALIM, A., AND ZHANG, A. R. Sampling is as Easy as Learning the Score: Theory for Diffusion Models with Minimal Data Assumptions. arXiv:2209.11215 (2022).
[6] CLARK, J. M. C., AND CAMERON, R. J. The Maximum Rate of Convergence of Discrete Approximations for Stochastic Differential Equations. In Stochastic Differential Systems Filtering and Control (Berlin, Heidelberg, 1980), Springer Berlin Heidelberg, pp. 162-171.
[7] DEBRABANT, K., KVÄRNØ, A., AND MATTSSON, N. C. Runge-Kutta Lawson schemes for Stochastic Differential Equations. BIT Numerical Mathematics 61 (2021), 381-409.
[8] DHARIWAL, P., AND NICHOL, A. Diffusion Models Beat GANs On Image Synthesis. Advances in Neural Information Processing Systems 34 (2021), 8780-8794.
[9] DOCKHORN, T., VAHDAT, A., AND KREIS, K. GENIE: Higher-order Denoising Diffusion Solvers. arXiv preprint arXiv:2210.05475 (2022).
[10] DOCKHORN, T., VAHDAT, A., AND KREIS, K. Score-Based Generative Modeling with Critically-Damped Langevin Diffusion. In International Conference on Learning Representations (ICLR) (2022).
[11] GONZALEZ, M., HAJRI, H., CANTAT, L., AND PETRECZKY, M. Noisy Learning of Neural ODEs Acts as a Robustness Locus Widening. In International Conference on Machine Learning (PODS Workshop) (2022).
[12] HO, J., JAIN, A., AND ABBEEL, P. Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems 33 (2020), 6840-6851.
[13] HO, J., SALIMANS, T., GRITSENKO, A., CHAN, W., NOROUZI, M., AND FLEET, D. J. Video Diffusion Models. arXiv:2204.03458 (2022).
[14] HOCHBRUCK, M., AND OSTERMANN, A. Exponential Integrators. Acta Numerica 19 (2010), 209-286.
[15] JOLICOEUR-MARTINEAU, A., LI, K., PICHÉ-TAILLEFER, R., KACHMAN, T., AND MITLIAGKAS, I. Gotta Go Fast When Generating Data With Score-Based Models. arXiv preprint arXiv:2105.14080 (2021).
[16] KARRAS, T., AITTALA, M., AILA, T., AND LAINE, S. Elucidating the Design Space of Diffusion-Based Generative Models. In Proc. NeurIPS (2022).
[17] KLOEDEN, P. E., AND PLATEN, E. Numerical Solution Of Stochastic Differential Equations. Stochastic Modelling And Applied Probability. Applications of Mathematics. Springer, 1992.
[18] KOMORI, Y. Exponential Runge-Kutta Methods for Stiff Stochastic Differential Equations. RIMS (2016), 128-140.
[19] KOMORI, Y., COHEN, D., AND BURRAGE, K. Weak Second Order Explicit Exponential Runge-Kutta Methods For Stochastic Differential Equations. SIAM Journal on Scientific Computing 39, 6 (2017), A2857-A2878.
[20] LIU, L., REN, Y., LIN, Z., AND ZHAO, Z. Pseudo Numerical Methods for Diffusion Models on Manifolds. arXiv preprint arXiv:2202.09778 (2022).
[21] LU, C., ZHENG, K., BAO, F., CHEN, J., LI, C., AND ZHU, J. Maximum Likelihood Training for Score-Based Diffusion ODEs by High Order Denoising Score Matching. In International Conference on Machine Learning (2022), PMLR, pp. 14429-14460.
[22] LU, C., ZHOU, Y., BAO, F., CHEN, J., LI, C., AND ZHU, J. DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. Advances in Neural Information Processing Systems (2022).
[23] LU, C., ZHOU, Y., BAO, F., CHEN, J., LI, C., AND ZHU, J. DPM-Solver++: Fast Solver For Guided Sampling Of Diffusion Probabilistic Models. arXiv preprint arXiv:2211.01095 (2022).