[47] Pearce, T., Rashid, T., Kanervisto, A., Bignell, D., Sun, M., Georgescu, R., Macua, S. V., Tan, S. Z., Momennejad, I., Hofmann, K. et al. [2023], 'Imitating human behaviour with diffusion models', arXiv preprint arXiv:2301.10677.
[48] Poole, B., Jain, A., Barron, J. T. and Mildenhall, B. [2023], Dreamfusion: Text-to-3d using 2d diffusion, in 'The Eleventh International Conference on Learning Representations'. URL: https://openreview.net/forum?id=FjNys5c7VvY
[49] Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. et al. [2021], Learning transferable visual models from natural language supervision, in 'International conference on machine learning', PMLR, pp. 8748-8763.
[50] Raissi, M. [2018], 'Deep hidden physics models: Deep learning of nonlinear partial differential equations', The Journal of Machine Learning Research 19(1), 932-955.
[51] Raissi, M., Perdikaris, P. and Karniadakis, G. E. [2019], 'Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations', Journal of Computational physics 378, 686-707.
[52] Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. and Chen, M. [2022], 'Hierarchical text-conditional image generation with clip latents', arXiv preprint arXiv:2204.06125.
[53] Reuss, M., Li, M., Jia, X. and Lioutikov, R. [2023], 'Goal-conditioned imitation learning using score-based diffusion policies', arXiv preprint arXiv:2304.02532.
[54] Rombach, R., Blattmann, A., Lorenz, D., Esser, P. and Ommer, B. [2022], High-resolution image synthesis with latent diffusion models, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition', pp. 10684-10695.
[55] Russel, S. [n.d.], 'Human compatible: Artificial intelligence and the problem of control (great britain: Allen lane, 2019). ibo van de poel, "embedding values in artificial intelligence (ai) systems.", *Minds and Machines* 30, 385-409.
[56] Russell, S. [1998], Learning agents for uncertain environments, in Proceedings of the eleventh annual conference on Computational learning theory, pp. 101-103.
[57] Russell, S. [2019], Human compatible: Artificial intelligence and the problem of control, Penguin.
[58] Särkkä, S. and Solin, A. [2019], Applied stochastic differential equations, Vol. 10, Cambridge University Press.
[59] Schramowski, P., Brack, M., Deiseroth, B. and Kersting, K. [2022], 'Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models', arXiv preprint arXiv:2211.05105.
[60] Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O. [2017], 'Proximal policy optimization algorithms', arXiv preprint arXiv:1707.06347.
[61] Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. and Ganguli, S. [2015], Deep unsupervised learning using nonequilibrium thermodynamics, in 'International Conference on Machine Learning', PMLR, pp. 2256-2265.
[62] Song, J., Meng, C. and Ermon, S. [2021], Denoising diffusion implicit models, in 'International Conference on Learning Representations'. URL: https://openreview.net/forum?id=St1giarCHLP
[63] Song, Y. and Ermon, S. [2019], Generative modeling by estimating gradients of the data distribution, in Proceedings of the 33rd International Conference on Neural Information Processing Systems', pp. 11918- 11930.
[64] Song, Y. and Ermon, S. [2020], 'Improved techniques for training score-based generative models', Advances in neural information processing systems 33, 12438-12448.
[65] Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S. and Poole, B. [n.d.], Score-based generative modeling through stochastic differential equations, in 'International Conference on Learning Representations'.
[66] Todorov, E. [2008], General duality between optimal control and estimation, in '2008 47th IEEE Conference on Decision and Control', IEEE, pp. 4286-4292.