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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 8876–8900 of 9051 papers

TitleStatusHype
Inverse Relationship Between Molecular Diversity And Resource Abundances—0
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm—0
The geometry of relative arbitrage—0
Requirements for Open-Ended Evolution in Natural and Artificial Systems—0
STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and SynchronyCode0
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing—0
Diversity improves performance in excitable networks—0
An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns—0
Diversity waves in collapse-driven population dynamics—0
Well-temperate phage: optimal bet-hedging against local environmental collapses—0
Model of human collective decision-making in complex environments—0
Parsimonious Labeling—0
Evaluation of Genotypic Diversity Measurements Exploited in Real-Coded Representation—0
Top-N recommendations in the presence of sparsity: An NCD-based approach—0
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
A guide through a family of phylogenetic dissimilarity measures among sites—0
Genetic Algorithms for multimodal optimization: a review—0
Measuring Feature Diversity in Native Language Identification—0
Watch and Learn: Semi-Supervised Learning for Object Detectors From Video—0
Material Classification With Thermal Imagery—0
Diversity-Induced Multi-View Subspace Clustering—0
Classifier Based Graph Construction for Video Segmentation—0
Diversity in Spectral Learning for Natural Language Parsing—0
Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge—0
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos—0
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