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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 3181–3190 of 9051 papers

TitleStatusHype
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading—0
Enhancing Recommendation Diversity by Re-ranking with Large Language Models—0
Bregman Centroid Guided Cross-Entropy Method—0
3D Neural Field Generation using Triplane Diffusion—0
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation—0
Enhancing Robustness of Pre-trained Language Model with Lexical Simplification—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification—0
Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integration—0
DiversiTree: A New Method to Efficiently Compute Diverse Sets of Near-Optimal Solutions to Mixed-Integer Optimization Problems—0
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