Results 71 to 80 of about 1,101,099 (365)
A Stochastic Treatment of Learning to Rank Scoring Functions
Web Search and Data Mining, 2020 Learning to Rank, a central problem in information retrieval, is a class of machine learning algorithms that formulate ranking as an optimization task.
Sebastian Bruch +3 more
semanticscholar +1 more source
Recommending GitHub Projects for Developer Onboarding
IEEE Access, 2018 Open-source platform (e.g., GitHub) creates a tremendous opportunity for developers to learn and build experience. Contribution to open source can be rewarding for developers and advocates the evolutionary progress of the open-source software.
Chao Liu +4 more
doaj +1 more source
Hashing as Tie-Aware Learning to Rank
, 2018 Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision
Bargal, Sarah Adel +3 more
core +1 more source
IEEE Access, 2019 Protein remote homology detection is one of the most challenging problems in the field of protein sequence analysis, which is an important step for both theoretical research (such as the understanding of structures and functions of proteins) and drug ...
Bin Liu, Yulin Zhu
doaj +1 more source
Early Exit Strategies for Learning-to-Rank Cascades
IEEE Access, 2023 The ranking pipelines of modern search platforms commonly exploit complex machine-learned models and have a significant impact on the query response time.
Francesco Busolin +5 more
doaj +1 more source
Learning to Rank Using Localized Geometric Mean Metrics
, 2017 Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query ...
King, Irwin, Lyu, Michael, Su, Yuxin
core +1 more source
Learning to efficiently rank [PDF]
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, 2010 It has been shown that learning to rank approaches are capable of learning highly effective ranking functions. However, these approaches have mostly ignored the important issue of efficiency. Given that both efficiency and effectiveness are important for real search engines, models that are optimized for effectiveness may not meet the strict efficiency
Lidan Wang, Jimmy Lin, Donald Metzler
openaire +2 more sources
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs [PDF]
IEEE Transactions on Image Processing, 2017 Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original
Kede Ma +4 more
semanticscholar +1 more source
Learning to Rank with BERT for Argument Quality Evaluation
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 The task of argument quality ranking, which identifies the quality of free text arguments, remains, to this day, a challenge. While most state-of-the-art initiatives use point-wise ranking methods and predict an absolute quality score for each argument ...
Charles-Olivier Favreau +2 more
doaj +1 more source
Single‐cell insights into the role of T cells in B‐cell malignancies
FEBS Letters, EarlyView.Single‐cell technologies have transformed our understanding of T cell–tumor cell interactions in B‐cell malignancies, revealing new T‐cell subsets, functional states, and immune evasion mechanisms. This Review synthesizes these findings, highlighting the roles of T cells in pathogenesis, progression, and therapy response, and underscoring their ...
Laura Llaó‐Cid
wiley +1 more source