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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 18411850 of 9051 papers

TitleStatusHype
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting0
Controllable Exploration of a Design Space via Interactive Quality Diversity0
Deep Reinforcement Learning with Quantum-inspired Experience Replay0
Controllable Inversion of Black-Box Face Recognition Models via Diffusion0
A systematic dataset generation technique applied to data-driven automotive aerodynamics0
Constrained Pseudo-market Equilibrium0
Constrained Interacting Submodular Groupings0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle0
Consistent Multiple Sequence Decoding0
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics0
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