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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 2761–2770 of 9051 papers

TitleStatusHype
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations—0
Diversity Matters When Learning From Ensembles—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity Measurement and Subset Selection for Instruction Tuning Datasets—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
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