| Are transformers effective for time series forecasting? A Zeng, M Chen, L Zhang, Q Xu Proceedings of the AAAI conference on artificial intelligence 37 (9), 11121 …, 2023 | 1891 | 2023 |
| Scinet: Time series modeling and forecasting with sample convolution and interaction M Liu, A Zeng, M Chen, Z Xu, Q Lai, L Ma, Q Xu Advances in Neural Information Processing Systems 35, 5816-5828, 2022 | 405 | 2022 |
| Are transformers effective for time series forecasting?(2022) A Zeng, M Chen, L Zhang, Q Xu arXiv preprint arXiv:2205.13504, 2023 | 26 | 2023 |
| FrAug: Frequency domain augmentation for time series forecasting M Chen, Z Xu, A Zeng, Q Xu arXiv preprint arXiv:2302.09292, 2023 | 12 | 2023 |
| HybridRepair: towards annotation-efficient repair for deep learning models Y Li, M Chen, Q Xu Proceedings of the 31st ACM SIGSOFT International Symposium on Software …, 2022 | 12 | 2022 |
| Hibug: On human-interpretable model debug M Chen, Y Li, Q Xu Advances in Neural Information Processing Systems 36, 4753-4766, 2023 | 11 | 2023 |
| An empirical study on the efficacy of deep active learning for image classification Y Li, M Chen, Y Liu, D He, Q Xu arXiv preprint arXiv:2212.03088, 2022 | 9 | 2022 |
| Evaluating text-to-image generative models: An empirical study on human image synthesis M Chen, Y Liu, J Yi, C Xu, Q Lai, H Wang, TY Ho, Q Xu arXiv preprint arXiv:2403.05125, 2024 | 6 | 2024 |
| DebugAgent: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging M Chen, C Zhao, Q Xu arXiv preprint arXiv:2501.16751, 2025 | 1 | 2025 |
| An Empirical Study on the Efficacy of Deep Active Learning Techniques YU LI, M Chen, Y Liu, D He, Q Xu | | |
