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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 23612370 of 9051 papers

TitleStatusHype
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generating Neural Networks with Neural NetworksCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Implementing Smart Contracts: The case of NFT-rental with pay-per-likeCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
BLESS: Benchmarking Large Language Models on Sentence SimplificationCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Generating Diverse and Meaningful CaptionsCode0
Blastocoel morphogenesis: a biophysics perspectiveCode0
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