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Results: 42
Number of items: 42
  • Open Access
    Shen, J., Snoek, C., Worring, M., Xiao, Z., & Zhen, X. (2023). Association Graph Learning for Multi-Task Classification with Category Shifts. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 7, pp. 4503-4516). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/1cc70be9fb6a83bc46cf4ac21a91e0b0-Abstract-Conference.html
  • Open Access
    Jing, M., Li, J., Snoek, C., & Zhen, X. (2023). Variational Model Perturbation for Source-Free Domain Adaptation. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 23, pp. 17173-17187). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/6d7a9f292360193eb530d693f7941c73-Abstract-Conference.html
  • Open Access
    Najdenkoska, I., Zhen, X., & Worring, M. (2023). Meta Learning To Bridge Vision and Language Models for Multimodal Few-Shot Learning. In The Eleventh International Conference on Learning Representations: ICLR 2023 https://doi.org/10.48550/arXiv.2302.14794
  • Open Access
    Du, Y., Shen, J., Zhen, X., & Snoek, C. G. M. (2023). EMO: Episodic Memory Optimization for Few-Shot Meta-Learning. Proceedings of Machine Learning Research, 232. https://doi.org/10.48550/arXiv.2306.05189
  • Open Access
    Jing, M., Zhen, X., Li, J., & Snoek, C. G. M. (2023). Order-preserving Consistency Regularization for Domain Adaptation and Generalization. In 2023 IEEE/CVF International Conference on Computer Vision: ICCV 2023 : Paris, France, 2-6 October 2023 : proceedings (pp. 18870-18881). IEEE Computer Society. https://doi.org/10.48550/arXiv.2309.13258, https://doi.org/10.1109/ICCV51070.2023.01734
  • Open Access
    van Sonsbeek, T., Zhen, X., Mahapatra, D., & Worring, M. (2023). Probabilistic Integration of Object Level Annotations in Chest X-ray Classification. In Proceedings, 2023 IEEE Winter Conference on Applications of Computer Vision: 3-7 January 2023, Waikoloa, Hawaii (pp. 3619-3629). (WACV; Vol. 2023). IEEE Computer Society. https://doi.org/10.1109/WACV56688.2023.00362
  • Open Access
    Sun, W., Du, Y., Zhen, X., Wang, F., Wang, L., & Snoek, C. G. M. (2023). MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks. Proceedings of Machine Learning Research, 202, 32847-32858. https://proceedings.mlr.press/v202/sun23b.html
  • Open Access
    Zhang, Z., Yannakoudakis, H., Zhen, X., & Shutova, E. (2023). CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics : Findings of EACL 2023: EACL 2023 : May 2-6, 2023 (pp. 2586-2596). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-eacl.196
  • Open Access
    Sun, H., Lu, X., Wang, H., Yin, Y., Zhen, X., Snoek, C. G. M., & Shao, L. (2023). Attentional prototype inference for few-shot segmentation. Pattern Recognition, 142, Article 109726. https://doi.org/10.1016/j.patcog.2023.109726
  • Open Access
    Shao, L., Shen, J., Worring, M., & Zhen, X. (2022). Variational Multi-Task Learning with Gumbel-Softmax Priors. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 25, pp. 21031-21042). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2021/hash/afd4836712c5e77550897e25711e1d96-Abstract.html
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