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Results: 37
Number of items: 37
  • Open Access
    Derakhshani, M. M., Zhen, X., Shao, L., & Snoek, C. G. M. (2021). Kernel Continual Learning. Proceedings of Machine Learning Research, 139, 2621-2631. https://proceedings.mlr.press/v139/derakhshani21a.html
  • Open Access
    Wang, H., Yang, Y., Cao, X., Zhen, X., Snoek, C., & Shao, L. (2021). Variational prototype inference for few-shot semantic segmentation. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 525-534). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00057
  • Open Access
    Du, Y., Holla, N., Zhen, X., Snoek, C. G. M., & Shutova, E. (2021). Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 5254-5268). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.409
  • Qi, M., Qin, J., Zhen, X., Huang, D., Yang, Y., & Luo, J. (2020). Few-Shot Ensemble Learning for Video Classification with SlowFast Memory Networks. In MM '20: proceedings of the 28th ACM International Conference on Multimedia : October 12-16, 2020, Virtual Event, USA (pp. 3007-3015). Association for Computing Machinery. https://doi.org/10.1145/3394171.3416269
  • Wang, H., Zhang, X., Hu, Y., Hu, Y., Yang, Y., Cao, X., & Zhen, X. (2020). Few-Shot Semantic Segmentation with Democratic Attention Networks. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings (Vol. XIII, pp. 730-746). (Lecture Notes in Computer Science; Vol. 12358). Springer. https://doi.org/10.1007/978-3-030-58601-0_43
  • Open Access
    Du, Y., Xu, J., Xiong, H., Qiu, Q., Zhen, X., Snoek, C. G. M., & Shao, L. (2020). Learning to Learn with Variational Information Bottleneck for Domain Generalization. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings (Vol. X, pp. 200-216). (Lecture Notes in Computer Science; Vol. 12355). Springer. https://doi.org/10.1007/978-3-030-58607-2_12
  • Open Access
    Zhen, X., Sun, H., Du, Y., Xu, J., Yin, Y., Shao, L., & Snoek, C. (2020). Learning to learn kernels with variational random features. Proceedings of Machine Learning Research, 119, 11409-11419. https://doi.org/10.48550/arXiv.2006.06707
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