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Found 16 papers, 1 papers with code
Can we predict QPP? An approach based on multivariate outliers
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents.
Selective Query Processing: a Risk-Sensitive Selection of System Configurations
To determine the ideal configurations to use on a per-query basis in real-world systems we developed a method in which a restricted number of possible configurations is pre-selected and then used in a meta-search engine that decides the best search configuration on a per query basis.
Effectiveness and Efficiency Trade-off in Selective Query Processing
In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e. g., when the term weighting function varies or when the expansion model varies).
iQPP: A Benchmark for Image Query Performance Prediction
To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image.
Multi-label Classification of Aircraft Heading Changes Using Neural Network to Resolve Conflicts
A controller needs to consider various types of information in order to solve a conflict.
Natural vs Balanced Distribution in Deep Learning on Whole Slide Images for Cancer Detection
The class distribution of data is one of the factors that regulates the performance of machine learning models.
Aggression Identification in Social Media: a Transfer Learning Based Approach
The evaluation part of this paper is based on the dataset provided by the TRAC shared task (Kumar et al., 2018a).
IRIT at TRAC 2020
This paper describes the participation of the IRIT team in the TRAC (Trolling, Aggression and Cyberbullying) 2020 shared task (Bhattacharya et al., 2020) on Aggression Identification and more precisely to the shared task in English language.
Getting Insights from a Large Corpus of Scientific Papers on Specialisted Comprehensive Topics -- the Case of COVID-19
We run these methodologies on two cases: the virus origin and the uses of existing drugs.
Forward and Backward Feature Selection for Query Performance Prediction
We found that: (1) our model based on a limited number of selected features is as good as more complex models for QPP and better than non-selective models; (2) our model is more efficient than complex models during inference time since it requires fewer features; (3) the predictive model is readable and understandable; and (4) one of our new QPP features is consistently selected across different collections, proving its usefulness.
IRIT at TRAC 2018
This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language.
Classification multi-label \`a grande dimension pour la d\'etection de concepts m\'edicaux (Large multi-label classification for medical concepts detection)
Dans ce papier, nous pr{\'e}sentons une m{\'e}thode pour associer de fa{\c{c}}on automatique des concepts {\`a} des images.
Non-convex Regularizations for Feature Selection in Ranking With Sparse SVM
Feature selection in learning to rank has recently emerged as a crucial issue.
