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Learning to Rank Reviewers for Pull Requests
IEEE Access, 2019 In pull-based software development, anyone who wants to contribute to a project can request integration of the code changes to the public repository by sending a pull request to the development team.
Xin Ye
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Learning to Rank for Multi-Step Ahead Time-Series Forecasting
IEEE Access, 2021 Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing ...
Jiuding Duan, Hisashi Kashima
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Learning-to-Rank with BERT in TF-Ranking
, 2020 This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
Han, Shuguang +3 more
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Learning to Rank for Plausible Plausibility [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 To appear in ACL ...
Zhongyang Li +2 more
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Siamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment
IEEE Access, 2019 2D image quality assessment (IQA) and stereoscopic 3D IQA are considered as two different tasks in the literature. In this paper, we present an index for both no-reference 2D and 3D IQA.
Yuzhen Niu +3 more
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Groupwise Learning to Rank Algorithm with Introduction of Activated Weighting [PDF]
Jisuanji kexue yu tansuo, 2022 Learning to rank (LtR) applies supervised machine learning (SML) technologies to the ranking problems, aiming at optimizing the relevance of input document list. As regard to previous studies on the deep ranking model, the calculation of the relevance of
LI Yuxuan, HONG Xuehai, WANG Yang, TANG Zhengzheng, BAN Yan
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Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration
IEEE Access, 2022 For automatic disease-severity-level estimation, a large-scale medical image dataset with level annotations is generally necessary. However, attaching absolute-level annotations (such as levels 0, 1, and 3) is very costly and even inaccurate due to the ...
Takeaki Kadota +6 more
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Learning To Rank Diversely At Airbnb
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 Search ranking, Diversity, e ...
Malay Haldar +5 more
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Hierarchical Entity Typing via Multi-level Learning to Rank [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings ...
Tongfei Chen +2 more
semanticscholar +1 more source