| Score-based generative modeling with critically-damped langevin diffusion T Dockhorn, A Vahdat, K Kreis arXiv preprint arXiv:2112.07068, 2021 | 108 | 2021 |
| A discussion on solving partial differential equations using neural networks T Dockhorn arXiv preprint arXiv:1904.07200, 2019 | 63 | 2019 |
| Align your latents: High-resolution video synthesis with latent diffusion models A Blattmann, R Rombach, H Ling, T Dockhorn, SW Kim, S Fidler, K Kreis Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 50 | 2023 |
| Genie: Higher-order denoising diffusion solvers T Dockhorn, A Vahdat, K Kreis Advances in Neural Information Processing Systems 35, 30150-30166, 2022 | 26 | 2022 |
| Differentially private diffusion models T Dockhorn, T Cao, A Vahdat, K Kreis arXiv preprint arXiv:2210.09929, 2022 | 21 | 2022 |
| SDXL: improving latent diffusion models for high-resolution image synthesis D Podell, Z English, K Lacey, A Blattmann, T Dockhorn, J Müller, J Penna, ... arXiv preprint arXiv:2307.01952, 2023 | 5 | 2023 |
| Latent Space Diffusion Models of Cryo-EM Structures K Kreis, T Dockhorn, Z Li, E Zhong arXiv preprint arXiv:2211.14169, 2022 | 5 | 2022 |
| Demystifying and generalizing binaryconnect T Dockhorn, Y Yu, E Sari, M Zolnouri, V Partovi Nia Advances in Neural Information Processing Systems 34, 13202-13216, 2021 | 5 | 2021 |
| Density deconvolution with normalizing flows T Dockhorn, JA Ritchie, Y Yu, I Murray arXiv preprint arXiv:2006.09396, 2020 | 3 | 2020 |
| Diffusion-based generative modeling for synthetic data generation systems and applications K Kreis, T Dockhorn, A Vahdat US Patent App. 17/959,915, 2023 | | 2023 |
| Generative Modeling with Neural Ordinary Differential Equations T Dockhorn University of Waterloo, 2019 | | 2019 |
| Distilling the Knowledge in Diffusion Models T Dockhorn, R Rombach, A Blatmann, Y Yu | | |
