[22] Ho, J. and Salimans, T. [n.d.], Classifier-free diffusion guidance, in 'NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications'.
[23] Hoogeboom, E., Satorras, V. G., Vignac, C. and Welling, M. [2022], Equivariant diffusion for molecule generation in 3d, in 'International Conference on Machine Learning', PMLR, pp. 8867-8887.
[24] Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W. and Zhu, S.-C. [2023], 'Diffusion-based generation, optimization, and planning in 3d scenes', arXiv preprint arXiv:2301.06015.
[25] Janner, M., Du, Y., Tenenbaum, J. and Levine, S. [2022], 'Planning with diffusion for flexible behavior synthesis'.
[26] Jing, B., Corso, G., Chang, J., Barzilay, R. and Jaakkola, T. S. [n.d.], Torsional diffusion for molecular conformer generation, in 'Advances in Neural Information Processing Systems'.
[27] Jing, B., Erives, E., Pao-Huang, P., Corso, G., Berger, B. and Jaakkola, T. S. [n.d.], Eigenfold: Generative protein structure prediction with diffusion models, in 'ICLR 2023-Machine Learning for Drug Discovery workshop'.
[28] Jordan, M. I. [2004], 'Graphical models', Statistical Science 19(1), 140-155. URL: http://www.jstor.org/stable/4144379
[29] Jost, J. [2012], *Partial differential equations*, Vol. 214, Springer Science & Business Media.
[30] Kappen, H. [2011], 'Optimal control theory and the linear bellman equation'.
[31] Kappen, H. J., Gómez, V. and Opper, M. [2012], 'Optimal control as a graphical model inference problem', Machine learning 87, 159-182.
[32] Karras, T., Aittala, M., Aila, T. and Laine, S. [n.d.], Elucidating the design space of diffusion-based generative models, in 'Advances in Neural Information Processing Systems'.
[33] Kingma, D. P. and Ba, J. [2014], 'Adam: A method for stochastic optimization', arXiv preprint arXiv:1412.6980.
[34] Kreyszig, E. [1978], 'Introduction to functional analysis with application, book'.
[35] Lax, P. D. and Terrell, M. S. [2017], Multivariable calculus with applications, Springer.
[36] Le Gall, J.-F. [2016], Brownian motion, martingales, and stochastic calculus, Springer.
[37] Levine, S. [2018], 'Reinforcement learning and control as probabilistic inference: Tutorial and review', arXiv preprint arXiv:1805.00909.
[38] Li, X., Thickstun, J., Gulrajani, I., Liang, P. S. and Hashimoto, T. B. [2022], 'Diffusion-lm improves controllable text generation', Advances in Neural Information Processing Systems 35, 4328-4343.
[39] Liang, Z., Mu, Y., Ding, M., Ni, F., Tomizuka, M. and Luo, P. [2023], 'Adaptdiffuser: Diffusion models as adaptive self-evolving planners', arXiv preprint arXiv:2302.01877.
[40] Magill, M., Qureshi, F. and de Haan, H. [2018], 'Neural networks trained to solve differential equations learn general representations', Advances in neural information processing systems 31.
[41] Melas-Kyriazi, L., Rupprecht, C., Laina, I. and Vedaldi, A. [2023], 'Realfusion: 360  $\{\backslash deg\}$  reconstruction of any object from a single image', arXiv preprint arXiv:2302.10663.
[42] Misra, D. [n.d.], 'Mish: A self regularized non-monotonic activation function'.
[43] Ng, A. Y., Russell, S. et al. [2000], Algorithms for inverse reinforcement learning., in 'Icml', Vol. 1, p. 2.
[44] Nichol, A. Q. and Dhariwal, P. [2021], Improved denoising diffusion probabilistic models, in 'International Conference on Machine Learning', PMLR, pp. 8162-8171.
[45] Øksendal, B. and Øksendal, B. [2003], Stochastic differential equations, Springer.
[46] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J. and Chintala, S. [2019], Pytorch: An imperative style, high-performance deep learning library, in 'Advances in Neural Information Processing Systems 32', Curran Associates, Inc., pp. 8024-8035.
URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf