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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 13611370 of 9051 papers

TitleStatusHype
ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid MotionsCode1
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
C^2: Scalable Auto-Feedback for LLM-based Chart GenerationCode1
grenedalf: population genetic statistics for the next generation of pool sequencingCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
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