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Learning a Fast Bipartite Ranker for Text Documents Using Lexicographical Rankers and ROC Curves
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017The design of powerful learning methods for addressing huge amounts of unstructured data, such as text documents, is a fundamental problem within the document analysis and recognition community. In this work, we propose FlexRank, a specially designed bipartite ranking algorithm for text documents using lexicographical ordering. FlexRank is based on the
Lucas de Souza Rodrigues +2 more
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Active query selection for learning rankers
Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception to this (Long et al. 2010) uses expected loss optimization (ELO) to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance ...
Mustafa Bilgic 0001, Paul N. Bennett
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Clip-Ranker: From Pointwise to Listwise Ranker for Multimodal Retrieval
2022Mengying Xu +3 more
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Meta-Ranker: Efficient Design of Combination Drug Products for Complex Diseases
IFAC-PapersOnLine, 2022Murat Cokol
exaly
Latent Space Neural Architecture Search via LambdaNDCGloss-Based Listwise Ranker
2023, Songyi Xiao
exaly