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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 3451–3460 of 9051 papers

TitleStatusHype
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces—0
Active Learning on Synthons for Molecular Design—0
Context-Enhanced Detector For Building Detection From Remote Sensing Images—0
Context-Dependent Diffusion Network for Visual Relationship Detection—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Artificial Intelligence Development Races in Heterogeneous Settings—0
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
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