Improving the pull requests review process using learning-to-rank algorithms
Sign Up to like & getrecommendations! Published in 2019 at "Empirical Software Engineering"
DOI: 10.1007/s10664-019-09696-8
Abstract: Collaborative software development platforms (such as GitHub and GitLab) have become increasingly popular as they have attracted thousands of external contributors to contribute to open source projects. The external contributors may submit their contributions via… read more here.
Keywords: pull; learning rank; pull requests; review process ... See more keywords
Incorporating query constraints for autoencoder enhanced ranking
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.03.068
Abstract: Abstract Learning to rank has been widely used in information retrieval tasks to construct ranking models for document retrieval. Existing learning to rank methods adopt supervised machine learning methods as core techniques and classical retrieval… read more here.
Keywords: retrieval; document; query constraints; learning rank ... See more keywords
LncRNA-disease association identification using graph auto-encoder and learning to rank
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac539
Abstract: Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload… read more here.
Keywords: graltr lda; disease; lncrna disease; disease associations ... See more keywords
Deep Neural Network Regularization for Feature Selection in Learning-to-Rank
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2902640
Abstract: Learning-to-rank is an emerging area of research for a wide range of applications. Many algorithms are devised to tackle the problem of learning-to-rank. However, very few existing algorithms deal with deep learning. Previous research depicts… read more here.
Keywords: tex math; neural network; learning rank; inline formula ... See more keywords
Automatic Estimation of Ulcerative Colitis Severity by Learning to Rank With Calibration
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3155769
Abstract: 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… read more here.
Keywords: severity; level; learning rank; estimation ... See more keywords
Learning Bregman Distance Functions for Structural Learning to Rank
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2017.2654250
Abstract: We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is… read more here.
Keywords: learning rank; distance; structural learning; distance functions ... See more keywords
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3098898
Abstract: Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collected from the online system. However, it has been noticed that previous… read more here.
Keywords: online performance; evaluator; learning rank; performance ... See more keywords
Distilled Neural Networks for Efficient Learning to Rank
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3152585
Abstract: Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles… read more here.
Keywords: neural network; neural networks; scoring time; learning rank ... See more keywords
Reducing correlation of random forest–based learning‐to‐rank algorithms using subsample size
Sign Up to like & getrecommendations! Published in 2019 at "Computational Intelligence"
DOI: 10.1111/coin.12213
Abstract: Learning‐to‐rank (LtR) has become an integral part of modern ranking systems. In this field, the random forest–based rank‐learning algorithms are shown to be among of the top performers. Traditionally, each tree of a random forest… read more here.
Keywords: correlation; random forest; forest based; learning rank ... See more keywords
Distributionally robust learning-to-rank under the Wasserstein metric
Sign Up to like & getrecommendations! Published in 2023 at "PLOS ONE"
DOI: 10.1371/journal.pone.0283574
Abstract: Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional… read more here.
Keywords: distributionally robust; drmrr; learning rank; ltr ... See more keywords
LEARNING TO RANK AND CLASSIFICATION OF BUG REPORTS USING SVM AND FEATURE EVALUATION
Sign Up to like & getrecommendations! Published in 2017 at "International Journal on Smart Sensing and Intelligent Systems"
DOI: 10.21307/ijssis-2017-254
Abstract: When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking… read more here.
Keywords: learning rank; source; reports using; bug report ... See more keywords