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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 23912400 of 9051 papers

TitleStatusHype
Generating Diverse Descriptions from Semantic GraphsCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Toward Improving Coherence and Diversity of Slogan GenerationCode0
Generating Diverse and Meaningful CaptionsCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel OptimizationCode0
Generalized Face Anti-spoofing via Finer Domain Partition and Disentangling Liveness-irrelevant FactorsCode0
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