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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 3201–3210 of 9051 papers

TitleStatusHype
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels—0
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI—0
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques—0
A Unifying Information-theoretic Perspective on Evaluating Generative Models—0
A local continuum model of cell-cell adhesion—0
Cultural Incongruencies in Artificial Intelligence—0
A unified view of generative models for networks: models, methods, opportunities, and challenges—0
Cultural Evaluations of Vision-Language Models Have a Lot to Learn from Cultural Theory—0
A Load Balanced Recommendation Approach—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
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