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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 771780 of 9051 papers

TitleStatusHype
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural NetworksCode1
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and BiasCode1
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regressionCode1
Restart Sampling for Improving Generative ProcessesCode1
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-StepCode1
RSMT: Real-time Stylized Motion Transition for CharactersCode1
grenedalf: population genetic statistics for the next generation of pool sequencingCode1
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
Explicit Syntactic Guidance for Neural Text GenerationCode1
GenPlot: Increasing the Scale and Diversity of Chart Derendering DataCode1
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