| POPQORN: Quantifying robustness of recurrent neural networks CY Ko, Z Lyu, L Weng, L Daniel, N Wong, D Lin International Conference on Machine Learning, 3468-3477, 2019 | 84 | 2019 |
| Fastened crown: Tightened neural network robustness certificates Z Lyu, CY Ko, Z Kong, N Wong, D Lin, L Daniel Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5037-5044, 2020 | 45 | 2020 |
| A conditional point diffusion-refinement paradigm for 3d point cloud completion Z Lyu, Z Kong, X Xu, L Pan, D Lin arXiv preprint arXiv:2112.03530, 2021 | 39 | 2021 |
| Accelerating diffusion models via early stop of the diffusion process Z Lyu, X Xu, C Yang, D Lin, B Dai arXiv preprint arXiv:2205.12524, 2022 | 21 | 2022 |
| Towards evaluating and training verifiably robust neural networks Z Lyu, M Guo, T Wu, G Xu, K Zhang, D Lin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 16 | 2021 |
| Guided diffusion model for adversarial purification J Wang, Z Lyu, D Lin, B Dai, H Fu arXiv preprint arXiv:2205.14969, 2022 | 11 | 2022 |
| Generative Diffusion Prior for Unified Image Restoration and Enhancement B Fei, Z Lyu, L Pan, J Zhang, W Yang, T Luo, B Zhang, B Dai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | | 2023 |
| Controllable Mesh Generation Through Sparse Latent Point Diffusion Models Z Lyu, J Wang, Y An, Y Zhang, D Lin, B Dai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | | 2023 |
