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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 951960 of 9051 papers

TitleStatusHype
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
Attributed Graph Clustering with Dual Redundancy ReductionCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros StudyCode1
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
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