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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 5351–5360 of 9051 papers

TitleStatusHype
SPARKE: Scalable Prompt-Aware Diversity Guidance in Diffusion Models via RKE Score—0
SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment—0
SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms—0
Sparse Antenna and Pulse Placement for Colocated MIMO Radar—0
Sparse Codesigned Communication and Radar Systems—0
Sparse Multi-Family Deep Scattering Network—0
Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution—0
Sparse Regression Codes exploit Multi-User Diversity without CSI—0
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models—0
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN—0
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