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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 1621–1630 of 9051 papers

TitleStatusHype
A Low Complexity Space-Frequency Multiuser Scheduling Algorithm—0
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—0
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels—0
A Unifying Information-theoretic Perspective on Evaluating Generative Models—0
A local continuum model of cell-cell adhesion—0
A unified view of generative models for networks: models, methods, opportunities, and challenges—0
A Load Balanced Recommendation Approach—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Accelerated Image-Aware Generative Diffusion Modeling—0
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples—0
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