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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 4921–4930 of 9051 papers

TitleStatusHype
COMET: Coverage-guided Model Generation For Deep Learning Library TestingCode0
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship—0
Towards Psychologically-Grounded Dynamic Preference Models—0
A Deep Generative Model for Feasible and Diverse Population Synthesis—0
Safe Perception -- A Hierarchical Monitor Approach—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
Tackling Neural Architecture Search With Quality Diversity OptimizationCode0
Smoothing Entailment Graphs with Language ModelsCode0
Streaming Algorithms for Diversity Maximization with Fairness ConstraintsCode0
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