| ediffi: Text-to-image diffusion models with an ensemble of expert denoisers Y Balaji, S Nah, X Huang, A Vahdat, J Song, Q Zhang, K Kreis, M Aittala, ... arXiv preprint arXiv:2211.01324, 2022 | 505 | 2022 |
| Fast sampling of diffusion models with exponential integrator Q Zhang, Y Chen International Conference on Learning Representations, 2022 | 256 | 2022 |
| Diffusion normalizing flow Q Zhang, Y Chen Advances in neural information processing systems 34, 16280-16291, 2021 | 84 | 2021 |
| gDDIM: Generalized denoising diffusion implicit models Q Zhang, M Tao, Y Chen International Conference on Learning Representations, 2022 | 79 | 2022 |
| Multi-marginal optimal transport and probabilistic graphical models I Haasler, R Singh, Q Zhang, J Karlsson, Y Chen IEEE Transactions on Information Theory 67 (7), 4647-4668, 2021 | 56 | 2021 |
| Variational Wasserstein gradient flow J Fan, Q Zhang, A Taghvaei, Y Chen ICML, 2021 | 47 | 2021 |
| Path integral sampler: a stochastic control approach for sampling Q Zhang, Y Chen International Conference on Learning Representations, 2021 | 47 | 2021 |
| Improving robustness via risk averse distributional reinforcement learning R Singh, Q Zhang, Y Chen Learning for Dynamics and Control, 958-968, 2020 | 45 | 2020 |
| DiffCollage: Parallel Generation of Large Content with Diffusion Models Q Zhang, J Song, X Huang, Y Chen, MY Liu Conference on Computer Vision and Pattern Recognition, 2023 | 39 | 2023 |
| Inference with aggregate data in probabilistic graphical models: An optimal transport approach R Singh, I Haasler, Q Zhang, J Karlsson, Y Chen IEEE Transactions on Automatic Control 67 (9), 4483-4497, 2022 | 32* | 2022 |
| Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation J Song, Q Zhang, H Yin, M Mardani, MY Liu, J Kautz, Y Chen, A Vahdat International Conference on Machine Learning, 2023 | 30 | 2023 |
| Improved order analysis and design of exponential integrator for diffusion models sampling Q Zhang, J Song, Y Chen arXiv preprint arXiv:2308.02157, 2023 | 9 | 2023 |
| Distrifusion: Distributed parallel inference for high-resolution diffusion models M Li, T Cai, J Cao, Q Zhang, H Cai, J Bai, Y Jia, K Li, S Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 7 | 2024 |
| An optimal control approach to particle filtering Q Zhang, A Taghvaei, Y Chen Automatica 151, 110894, 2023 | 6 | 2023 |
| Incremental inference of collective graphical models R Singh, I Haasler, Q Zhang, J Karlsson, Y Chen IEEE Control Systems Letters 5 (2), 421-426, 2020 | 6 | 2020 |
| Inference of aggregate hidden Markov models with continuous observations Q Zhang, R Singh, Y Chen IEEE Control Systems Letters 6, 2377-2382, 2022 | 3* | 2022 |
| Learning hidden Markov models from aggregate observations R Singh, Q Zhang, Y Chen Automatica 137, 110100, 2022 | 3 | 2022 |
| Techniques for denoising diffusion using an ensemble of expert denoisers Y Balaji, TO Aila, M Aittala, B Catanzaro, X Huang, TT Karras, K Kreis, ... US Patent App. 18/485,239, 2024 | | 2024 |
| Condition-Aware Neural Network for Controlled Image Generation H Cai, M Li, Q Zhang, MY Liu, S Han Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | | 2024 |
| An optimal control approach to particle filtering on Lie groups B Yuan, Q Zhang, Y Chen IEEE Control Systems Letters 7, 1195-1200, 2022 | | 2022 |
