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  • Gou J, Liu Y, Shao M and Suel T. (2024). Beyond Quantile Methods: Improved Top-K Threshold Estimation for Traditional and Learned Sparse Indexes 2024 IEEE International Conference on Big Data (BigData). 10.1109/BigData62323.2024.10825349. 979-8-3503-6248-0. (709-716).

    https://ieeexplore.ieee.org/document/10825349/

  • Mallia A, Suel T and Tonellotto N. Faster Learned Sparse Retrieval with Block-Max Pruning. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2411-2415).

    https://doi.org/10.1145/3626772.3657906

  • Mackenzie J, Trotman A and Lin J. (2022). Efficient Document-at-a-time and Score-at-a-time Query Evaluation for Learned Sparse Representations. ACM Transactions on Information Systems. 41:4. (1-28). Online publication date: 31-Oct-2023.

    https://doi.org/10.1145/3576922

  • Yafay E and Altingovde I. Faster Dynamic Pruning via Reordering of Documents in Inverted Indexes. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2001-2005).

    https://doi.org/10.1145/3539618.3591987

  • Lassance C and Clinchant S. An Efficiency Study for SPLADE Models. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2220-2226).

    https://doi.org/10.1145/3477495.3531833

  • Siedlaczek M, Mallia A and Suel T. Using Conjunctions for Faster Disjunctive Top-k Queries. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. (917-927).

    https://doi.org/10.1145/3488560.3498489

  • Shao J, Qiao Y, Ji S and Yang T. Window Navigation with Adaptive Probing for Executing BlockMax WAND. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2323-2327).

    https://doi.org/10.1145/3404835.3463109