Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 298
Number of items: 298
  • Open Access
    Ambekar, S., Xiao, Z., Shen, J., Zhen, X., & Snoek, C. G. M. (2024). Probabilistic Test-Time Generalization by Variational Neighbor-Labeling. Proceedings of Machine Learning Research, 274, 832-851. https://proceedings.mlr.press/v274/ambekar25a.html
  • Open Access
    Xiao, Z., Shen, J., Derakhshani, M. M., Liao, S., & Snoek, C. G. M. (2024). Any-Shift Prompting for Generalization over Distributions. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 13849-13860). IEEE Computer Society. https://doi.org/10.48550/arXiv.2402.10099, https://doi.org/10.1109/CVPR52733.2024.01314
  • Open Access
    Zhang, Y., Doughty, H., & Snoek, C. G. M. (2024). Low-Resource Vision Challenges for Foundation Models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 21956-21966). IEEE Computer Society. https://doi.org/10.48550/arXiv.2401.04716, https://doi.org/10.1109/CVPR52733.2024.02073
  • Open Access
    Zhang, Y. (2024). Multimodal learning under visually challenging conditions. [Thesis, fully internal, Universiteitsbibliotheek].
  • Open Access
    Hu, V. T., Wu, D., Asano, Y. M., Mettes, P., Fernández-Méndez, F., Ommer, B., & Snoek, C. G. M. (2024). Flow Matching for Conditional Text Generation in a Few Sampling Steps. In Y. Graham, & M. Purver (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics: proceedings of the conference : EACL 2024 : March 17-22, 2024 (Vol. 2, pp. 380-392). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-short.33
  • Open Access
    Rastegar, S., Doughty, H., & Snoek, C. G. M. (2024). Background no more: Action recognition across domains by causal interventions. Computer Vision and Image Understanding, 242, Article 103975. https://doi.org/10.1016/j.cviu.2024.103975
  • Open Access
    Hu, V. T., Zhang, W., Tang, M., Mettes, P., Zhao, D., & Snoek, C. (2024). Latent Space Editing in Transformer-Based Flow Matching. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence: AAAI-2024 (Vol. 3, pp. 2247-2255). AAAI Press. https://doi.org/10.1609/aaai.v38i3.27998
  • 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
    Sosnovik, I. (2023). Symmetry-based learning from limited data. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hu, T. (2023). Label-efficient learning to see. [Thesis, fully internal, Universiteit van Amsterdam].
Page 3 of 30