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An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance.

, , , and . ICTIR, page 75-78. ACM, (2019)

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Learning Representations for Axis-Aligned Decision Forests through Input Perturbation., , and . CoRR, (2020)Modeling Text with Decision Forests using Categorical-Set Splits., , , , and . CoRR, (2020)ReNeuIR: Reaching Efficiency in Neural Information Retrieval., , and . SIGIR, page 3462-3465. ACM, (2022)Preface WI-IAT 2023., , , , and . WI/IAT, page xviii-xxi. IEEE, (2023)An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance., , , and . ICTIR, page 75-78. ACM, (2019)TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank., , , , , , , , , and . CoRR, (2018)Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning., , , , , , , , and . SIGIR, page 1419-1420. ACM, (2019)Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial., , , and . ICTIR, page 253-254. ACM, (2019)Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library., , , and . KDD, page 4068-4077. ACM, (2023)An Analysis of Fusion Functions for Hybrid Retrieval., , and . ACM Trans. Inf. Syst., 42 (1): 20:1-20:35 (January 2024)