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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 40814090 of 9051 papers

TitleStatusHype
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity AlignmentCode0
From Local to Global: Navigating Linguistic Diversity in the African ContextCode0
Multi-Task Multi-Behavior MAP-Elites0
Multidimensional Fairness in Paper Recommendation0
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters0
Emergent cooperative behavior in transient compartments0
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas0
Reconstructing seen images from human brain activity via guided stochastic search0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics0
Class-Balancing Diffusion ModelsCode1
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