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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 901910 of 9051 papers

TitleStatusHype
Optimizing Readability Using Genetic AlgorithmsCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware SynthesisCode1
Progressive Spatio-Temporal Prototype Matching for Text-Video RetrievalCode1
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face SynthesisCode1
DISCO: Distilling Counterfactuals with Large Language ModelsCode1
Data Curation Alone Can Stabilize In-context LearningCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
APOLLO: An Optimized Training Approach for Long-form Numerical ReasoningCode1
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