| Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting H Wu, J Xu, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2021 | 2328 | 2021 |
| TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis H Wu, T Hu, Y Liu, H Zhou, J Wang, M Long International Conference on Learning Representations (ICLR), 2022 | 1002 | 2022 |
| Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy J Xu, H Wu, J Wang, M Long International Conference on Learning Representations (ICLR), 2021 | 624 | 2021 |
| iTransformer: Inverted transformers are effective for time series forecasting Y Liu, T Hu, H Zhang, H Wu, S Wang, L Ma, M Long International Conference on Learning Representations (ICLR), 2023 | 582 | 2023 |
| Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting Y Liu, H Wu, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2022 | 522 | 2022 |
| PredRNN: A recurrent neural network for spatiotemporal predictive learning Y Wang, H Wu, J Zhang, Z Gao, J Wang, P Yu, M Long IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 | 454 | 2022 |
| MotionRNN: A flexible model for video prediction with spacetime-varying motions H Wu, Z Yao, J Wang, M Long IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 180 | 2021 |
| TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting S Wang, H Wu, X Shi, T Hu, H Luo, L Ma, JY Zhang, J Zhou International Conference on Learning Representations (ICLR), 2023 | 129 | 2023 |
| Flowformer: Linearizing Transformers with Conservation Flows H Wu, J Wu, J Xu, J Wang, M Long International Conference on Machine Learning (ICML), 2022 | 104 | 2022 |
| SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling J Dong, H Wu, H Zhang, L Zhang, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2023 | 100 | 2023 |
| Interpretable weather forecasting for worldwide stations with a unified deep model H Wu, H Zhou, M Long, J Wang Nature Machine Intelligence 5 (6), 602-611, 2023 | 72 | 2023 |
| Supported Policy Optimization for Offline Reinforcement Learning J Wu, H Wu, Z Qiu, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2022 | 72 | 2022 |
| Solving High-Dimensional PDEs with Latent Spectral Models H Wu, T Hu, H Luo, J Wang, M Long International Conference on Machine Learning (ICML), 2023 | 42 | 2023 |
| Timexer: Empowering transformers for time series forecasting with exogenous variables Y Wang, H Wu, J Dong, Y Liu, Y Qiu, H Zhang, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2024 | 33 | 2024 |
| Transolver: A Fast Transformer Solver for PDEs on General Geometries H Wu, H Luo, H Wang, J Wang, M Long International Conference on Machine Learning (ICML), 2024 | 33 | 2024 |
| Deep time series models: A comprehensive survey and benchmark Y Wang, H Wu, J Dong, Y Liu, M Long, J Wang arXiv preprint arXiv:2407.13278, 2024 | 30 | 2024 |
| TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling J Dong, H Wu, Y Wang, Y Qiu, L Zhang, J Wang, M Long International Conference on Machine Learning (ICML), 2024 | 11 | 2024 |
| ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised Predictive Learning Z Yao, Y Wang, H Wu, J Wang, M Long IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 | 11 | 2023 |
| Unisolver: PDE-conditional transformers are universal PDE solvers H Zhou, Y Ma, H Wu, H Wang, M Long arXiv preprint arXiv:2405.17527, 2024 | 7 | 2024 |
| RoPINN: Region Optimized Physics-Informed Neural Networks H Wu, H Luo, Y Ma, J Wang, M Long Advances in Neural Information Processing Systems (NeurIPS), 2024 | 7 | 2024 |
Mingsheng LongAssociate Professor, Tsinghua UniversityZweryfikowany adres z tsinghua.edu.cn
