Results 121 to 130 of about 1,101,099 (365)
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
International Journal of Information and Communication Technology Research, 2014 One of the important factors that affects the performance of Cross Language Information Retrieval(CLIR) is the quality of translations being employed in CLIR. In order to improve the quality of translations, it is important to exploit available resources
Hosein Azarbonyad +2 more
doaj
Effective Lightweight Learning-to-Rank Method Using Unified Term Impacts
IEEE Access, 2020 In this study, we propose and evaluate a novel learning-to-rank (L2R) approach that produces results on par with those of the state-of-the-art L2R methods while being computationally effective.
Sheila Da N. Silva +3 more
doaj +1 more source
Learning to Rank from Samples of Variable Quality [PDF]
, 2018 Training deep neural networks requires many training samples, but in practice, training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of ...
Dehghani, Mostafa, Kamps, Jaap
core +1 more source
Interplay between RNA‐protein interactions and RNA structures in gene regulation
FEBS Open Bio, EarlyView.Methodological advances in mapping transcriptome‐wide RNA‐protein interactions and RNA structures have started to uncover the potential of RNP conformations in gene regulation. Competing RNA–RNA, RNA‐protein and protein–protein interactions shape the compaction and function of RNPs throughout their lifetime and may provide novel therapeutic targets in ...
Jenni Rapakko +2 more
wiley +1 more source
Learning to rank quantum circuits for hardware-optimized performance enhancement [PDF]
QuantumWe introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware.
Gavin S. Hartnett +5 more
doaj +1 more source
Learning to Rank with Selection Bias in Personal Search
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016 Click-through data has proven to be a critical resource for improving search ranking quality. Though a large amount of click data can be easily collected by search engines, various biases make it difficult to fully leverage this type of data. In the past,
Xuanhui Wang +3 more
semanticscholar +1 more source
Cloning, expression, purification, and structural modeling of the Chandipura virus matrix protein
FEBS Open Bio, EarlyView.This research protocol offers a guide for the cloning, expression, and purification of the Chandipura virus matrix protein using E. coli. It also includes a step‐by‐step procedure for cloning, expressing, and conducting fluorescence imaging of GFP‐fused Chandipura virus matrix protein in mammalian cell lines.
Mariana Grieben
wiley +1 more source
An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance
International Conference on the Theory of Information Retrieval, 2019 One of the challenges of learning-to-rank for information retrieval is that ranking metrics are not smooth and as such cannot be optimized directly with gradient descent optimization methods.
Sebastian Bruch +3 more
semanticscholar +1 more source
Homologous expression and purification of human HAX‐1 for structural studies
FEBS Open Bio, EarlyView.This research protocol provides detailed instructions for cloning, expressing, and purifying large quantities of the intrinsically disordered human HAX‐1 protein, N‐terminally fused to a cleavable superfolder GFP, from mammalian cells. HAX‐1 is predicted to undergo posttranslational modifications and to interact with membranes, various cellular ...
Mariana Grieben
wiley +1 more source
Exam‐level analysis of lecture capture viewing and student exam performance
FEBS Open Bio, EarlyView.This study asks how lecture capture relates to exam performance in biology courses with three noncumulative exams. Mixed‐effects modeling separated between‐students from within‐student effects on exam %. Students who watched more recordings earned higher grades, but viewing spikes above a student's own mean, consistent with cramming, predicted small ...
Kirk Hillsley
wiley +1 more source