Results 11 to 20 of about 1,101,099 (365)
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework. [PDF]
Bioinformatics, 2019 Motivation De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins.
Yang H, Chi H, Zeng WF, Zhou WJ, He SM.
europepmc +2 more sources
Learning to rank figures within a biomedical article. [PDF]
PLoS ONE, 2014 Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their ...
Feifan Liu, Hong Yu
doaj +2 more sources
Learning to Rank for Uplift Modeling [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Causal classification concerns the estimation of the net effect of a treatment on an outcome of interest at the instance level, i.e., of the individual treatment effect (ITE). For binary treatment and outcome variables, causal classification models produce ITE estimates that essentially allow one to rank instances from a large positive effect to a ...
Devriendt, Floris +3 more
openaire +4 more sources
Reducing Disparate Exposure in Ranking: A Learning To Rank Approach [PDF]
The Web Conference, 2018 Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, educational ...
Meike Zehlike, Carlos Castillo
openalex +3 more sources
Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations. [PDF]
JMIR Med Inform, 2016 Chen J, Zheng J, Yu H.
europepmc +2 more sources
A General Framework for Counterfactual Learning-to-Rank
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2018 Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias.
Aman Agarwal +3 more
openalex +3 more sources
DNorm: disease name normalization with pairwise learning to rank. [PDF]
Bioinformatics, 2013 Leaman R, Islamaj Dogan R, Lu Z.
europepmc +3 more sources
Lero: A Learning-to-Rank Query Optimizer [PDF]
Proceedings of the VLDB Endowment, 2023 A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model ...
Rong Zhu +6 more
semanticscholar +1 more source
Learning to Rank in Generative Retrieval [PDF]
AAAI Conference on Artificial Intelligence, 2023 Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target.
Yongqing Li +4 more
semanticscholar +1 more source
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications.
Jiduan Liu +8 more
semanticscholar +1 more source