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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

Showing 16611670 of 9051 papers

TitleStatusHype
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Attribute Diversity Determines the Systematicity Gap in VQACode0
Attributed Graph Clustering via Adaptive Graph ConvolutionCode0
Insect Identification in the Wild: The AMI DatasetCode0
Attribute-aware Diversification for Sequential RecommendationsCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
InstaNAS: Instance-aware Neural Architecture SearchCode0
Attribute Alignment: Controlling Text Generation from Pre-trained Language ModelsCode0
Attraction-Repulsion clustering with applications to fairnessCode0
Attesting Distributional Properties of Training Data for Machine LearningCode0
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