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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 18511860 of 9051 papers

TitleStatusHype
ControlVAE: Controllable Variational Autoencoder0
Constrained Interacting Submodular Groupings0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle0
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble0
Consistent Multiple Sequence Decoding0
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics0
Controlling biases and diversity in diverse image-to-image translation0
Controlling Character Motions without Observable Driving Source0
AI Competitions and Benchmarks: Competition platforms0
Consistent Flow Distillation for Text-to-3D Generation0
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