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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 4076–4100 of 9051 papers

TitleStatusHype
Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020—0
Evolutionary multiplayer games on graphs with edge diversity—0
Single- and Multi-Objective Evolutionary Algorithms for the Knapsack Problem with Dynamically Changing Constraints—0
Competition Dynamics in the Meme Ecosystem—0
Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report—0
Aggregation of Classifiers: A Justifiable Information Granularity Approach—0
Evolutionary Multi-Objective Diversity Optimization—0
Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package—0
good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval—0
Evolutionary Multimodal Optimization: A Short Survey—0
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic Programming—0
Evolutionary Landscape and Management of Population Diversity—0
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1—0
Artificial intelligence and the internal processes of creativity—0
Evolutionary features in a minimal physical system: directionality, diversity, selection, growth, inheritance, and adaptation—0
Evolutionary Dynamic Optimization and Machine Learning—0
Comparison of machine learning models applied on anonymized data with different techniques—0
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning—0
GrACE: Generation using Associated Code Edits—0
Evolutionary drivers, morphological evolution and diversity dynamics of a surviving mammal clade: cainotherioids at the Eocene--Oligocene transition—0
Machine Learning in Nuclear Physics—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning—0
Evolutionary Diversity Optimization Using Multi-Objective Indicators—0
Comparison of classifiers in challenge scheme—0
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