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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 391400 of 9051 papers

TitleStatusHype
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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