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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 23712380 of 9051 papers

TitleStatusHype
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Generating Natural Language Adversarial ExamplesCode0
Black-Box Testing of Deep Neural Networks Through Test Case DiversityCode0
Generating Diverse and Meaningful CaptionsCode0
Generating Diverse and High-Quality Texts by Minimum Bayes Risk DecodingCode0
Generating Diverse Descriptions from Semantic GraphsCode0
Toward Improving Coherence and Diversity of Slogan GenerationCode0
Bipartite Graph Diffusion Model for Human Interaction GenerationCode0
Generating Diverse and Accurate Visual Captions by Comparative Adversarial LearningCode0
Bioplastic Design using Multitask Deep Neural NetworksCode0
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