| Understanding diffusion models: A unified perspective C Luo arXiv preprint arXiv:2208.11970, 2022 | 184 | 2022 |
| Intriguing properties of contrastive losses T Chen, C Luo, L Li Advances in Neural Information Processing Systems 34, 11834-11845, 2021 | 158 | 2021 |
| 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 |
| Towards a unified neural architecture for visual recognition and reasoning C Luo, B Gong, T Chen, C Sun arXiv preprint arXiv:2311.06386, 2023 | 1 | 2023 |
| Towards learning implicit symbolic representation for visual reasoning C Sun, C Luo, X Zhou, A Arnab, C Schmid | 1 | 2022 |
| 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 |
| 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 |
| Text-Aware Diffusion Policies C Luo, C Sun | | 2023 |
David DuvenaudAssociate Professor, University of TorontoVerified email at cs.toronto.edu
