| Convolutional LSTM network: A machine learning approach for precipitation nowcasting X Shi, Z Chen, H Wang, DY Yeung, WK Wong, W Woo Advances in neural information processing systems 28, 2015 | 10804 | 2015 |
| Deep learning for precipitation nowcasting: A benchmark and a new model X Shi, Z Gao, L Lausen, H Wang, DY Yeung, W Wong, W Woo Advances in neural information processing systems 30, 2017 | 1037 | 2017 |
| Gaan: Gated attention networks for learning on large and spatiotemporal graphs J Zhang, X Shi, J Xie, H Ma, I King, DY Yeung arXiv preprint arXiv:1803.07294, 2018 | 840 | 2018 |
| Dynamic key-value memory networks for knowledge tracing J Zhang, X Shi, I King, DY Yeung Proceedings of the 26th international conference on World Wide Web, 765-774, 2017 | 832 | 2017 |
| Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems J Zhang, X Shi, S Zhao, I King arXiv preprint arXiv:1905.13129, 2019 | 296 | 2019 |
| Spatiotemporal modeling for crowd counting in videos F Xiong, X Shi, DY Yeung Proceedings of the IEEE international conference on computer vision, 5151-5159, 2017 | 251 | 2017 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... Journal of Machine Learning Research 21 (23), 1-7, 2020 | 248 | 2020 |
| Earthformer: Exploring space-time transformers for earth system forecasting Z Gao, X Shi, H Wang, Y Zhu, YB Wang, M Li, DY Yeung Advances in Neural Information Processing Systems 35, 25390-25403, 2022 | 172 | 2022 |
| Relational stacked denoising autoencoder for tag recommendation H Wang, X Shi, DY Yeung Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 163 | 2015 |
| Machine learning for spatiotemporal sequence forecasting: A survey X Shi, DY Yeung arXiv preprint arXiv:1808.06865, 2018 | 131 | 2018 |
| Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks H Wang, X Shi, DY Yeung Advances in neural information processing systems 29, 2016 | 127 | 2016 |
| Natural-parameter networks: A class of probabilistic neural networks H Wang, X Shi, DY Yeung Advances in neural information processing systems 29, 2016 | 93 | 2016 |
| Symbolic music generation with transformer-gans A Muhamed, L Li, X Shi, S Yaddanapudi, W Chi, D Jackson, R Suresh, ... Proceedings of the AAAI conference on artificial intelligence 35 (1), 408-417, 2021 | 85 | 2021 |
| Benchmarking multimodal automl for tabular data with text fields X Shi, J Mueller, N Erickson, M Li, AJ Smola arXiv preprint arXiv:2111.02705, 2021 | 83* | 2021 |
| Relational deep learning: A deep latent variable model for link prediction H Wang, X Shi, DY Yeung Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 82 | 2017 |
| Visual prompt tuning for test-time domain adaptation Y Gao, X Shi, Y Zhu, H Wang, Z Tang, X Zhou, M Li, DN Metaxas arXiv preprint arXiv:2210.04831, 2022 | 79 | 2022 |
| Xtab: Cross-table pretraining for tabular transformers B Zhu, X Shi, N Erickson, M Li, G Karypis, M Shoaran arXiv preprint arXiv:2305.06090, 2023 | 67 | 2023 |
| Layoutdiffuse: Adapting foundational diffusion models for layout-to-image generation J Cheng, X Liang, X Shi, T He, T Xiao, M Li arXiv preprint arXiv:2302.08908, 2023 | 61 | 2023 |
| Parameter-efficient fine-tuning design spaces J Chen, A Zhang, X Shi, M Li, A Smola, D Yang arXiv preprint arXiv:2301.01821, 2023 | 61 | 2023 |
| Towards geospatial foundation models via continual pretraining M Mendieta, B Han, X Shi, Y Zhu, C Chen Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 57 | 2023 |
Dit-Yan YeungChair Professor, Department of CSE, HKUST, Hong KongAdresse e-mail validée de cse.ust.hk