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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 29612970 of 9051 papers

TitleStatusHype
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires0
Dynamic Scale Inference by Entropy Minimization0
Dynamics of Algorithmic Content Amplification on TikTok0
A Parameterized Family of Meta-Submodular Functions0
Dynamics of Transient Structure in In-Context Linear Regression Transformers0
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks0
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)0
Channel-wise Noise Scheduled Diffusion for Inverse Rendering in Indoor Scenes0
3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes0
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