| A prototype-oriented framework for unsupervised domain adaptation K Tanwisuth, X Fan, H Zheng, S Zhang, H Zhang, B Chen, M Zhou Advances in Neural Information Processing Systems 34, 17194-17208, 2021 | 73 | 2021 |
| Contextual Dropout: An Efficient Sample-Dependent Dropout Module X Fan, S Zhang, K Tanwisuth, X Qian, M Zhou International Conference on Learning Representations, 2021 | 29 | 2021 |
| Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings D Wang, D Gao, H Zhao, H Zheng, K Tanwisuth, B Chen, M Zhou International Conference on Learning Representations, 2022 | 26 | 2022 |
| POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models K Tanwisuth, S Zhang, H Zheng, P He, M Zhou International Conference on Machine Learning, 2023 | 13 | 2023 |
| Alignment attention by matching key and query distributions S Zhang, X Fan, H Zheng, K Tanwisuth, M Zhou Advances in Neural Information Processing Systems 34, 13444-13457, 2021 | 7 | 2021 |
| A prototype-oriented clustering for domain shift with source privacy K Tanwisuth, S Zhang, P He, M Zhou arXiv preprint arXiv:2302.03807, 2023 | 2 | 2023 |
| A prototype-oriented framework for deep transfer learning applications K Tanwisuth | | 2023 |
