Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 1,025
Number of items: 1,025
  • Open Access
    Hendriksen, M. Y. (2024). Multimodal machine learning for information retrieval: A vision and language perspective. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Yuan, Y., Siro, C., Aliannejadi, M., de Rijke, M., & Lam, W. (2024). Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search. In WWW '24: Proceedings of the ACM Web Conference 2024 : May 13-17, 2024, Singapore, Singapore (pp. 1474-1485). The Association for Computing Machinery. https://doi.org/10.48550/arXiv.2402.07742, https://doi.org/10.1145/3589334.3645483
  • Open Access
    Chen, X., Liao, B., Qi, J., Eustratiadis, P., Monz, C., Bisazza, A., & de Rijke, M. (2024). The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2024: EMNLP 2024 : November 12-16, 2024 (pp. 1691-1706). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.92
  • Open Access
    Meng, C., Arabzadeh, N., Askari, A., Aliannejadi, M., & de Rijke, M. (2024). Ranked List Truncation for Large Language Model-based Re-Ranking. In SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval : July 14-18, 2024, Washington, DC, USA (pp. 141-151). Association for Computing Machinery. https://doi.org/10.1145/3626772.3657864
  • Open Access
    Khandel, P., Yates, A., Varbanescu, A.-L., de Rijke, M., & Pimentel, A. (2024). Distillation vs. Sampling for Efficient Training of Learning to Rank Models. In ICTIR '24: Proceedings of the 2024 ACM SIGIR International Conference on the Theory of Information Retrieval : July 13, 2024 Washington, DC, USA (pp. 51-60). The Association for Computing Machinery. https://doi.org/10.1145/3664190.3672527
  • Open Access
    Sprangers, O., Wadman, W., Schelter, S., & de Rijke, M. (2024). Hierarchical forecasting at scale. International Journal of Forecasting, 40(4), 1689-1700. https://doi.org/10.1016/j.ijforecast.2024.02.006
  • Open Access
    Huang, J. (2024). Learning recommender systems from biased user interactions. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Sprangers, O. R. (2024). Efficient and accurate forecasting in large-scale settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wang, Z. (2024). Beyond boundaries: Towards generalizable information extraction frameworks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Bleeker, M. J. R. (2024). Multi-modal learning algorithms for sequence modeling and representation learning. [Thesis, fully internal, Universiteit van Amsterdam].
Page 6 of 103