| Understanding diffusion models: A unified perspective C Luo arXiv preprint arXiv:2208.11970, 2022 | 209 | 2022 |
| Intriguing properties of contrastive losses T Chen, C Luo, L Li Advances in Neural Information Processing Systems 34, 11834-11845, 2021 | 168 | 2021 |
| Understanding diffusion models: A unified perspective. arXiv 2022 C Luo arXiv preprint arXiv:2208.11970, 0 | 8 | |
| Data augmentation via structured adversarial perturbations C Luo, H Mobahi, S Bengio arXiv preprint arXiv:2011.03010, 2020 | 3 | 2020 |
| Does visual pretraining help end-to-end reasoning? C Sun, C Luo, X Zhou, A Arnab, C Schmid Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Scalable recommender systems through recursive evidence chains E Tragas, C Luo, M Gazeau, K Luk, D Duvenaud arXiv preprint arXiv:1807.02150, 2018 | 1 | 2018 |
| Text-Aware Diffusion for Policy Learning C Luo, M He, Z Zeng, C Sun arXiv preprint arXiv:2407.01903, 2024 | | 2024 |
| Self-Correcting Self-Consuming Loops for Generative Model Training N Gillman, M Freeman, D Aggarwal, CH Hsu, C Luo, Y Tian, C Sun arXiv preprint arXiv:2402.07087, 2024 | | 2024 |
| Towards A Unified Neural Architecture for Visual Recognition and Reasoning C Luo, B Gong, T Chen, C Sun arXiv preprint arXiv:2311.06386, 2023 | | 2023 |
| Text-Aware Diffusion Policies C Luo, C Sun | | |
| Towards Learning Implicit Symbolic Representation for Visual Reasoning C Sun, C Luo, X Zhou, A Arnab, C Schmid | | |
David DuvenaudAssociate Professor, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
David DuvenaudAssociate Professor, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
Maxime GazeauBorealis AI在 borealisai.com 的电子邮件经过验证