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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 27912800 of 9051 papers

TitleStatusHype
Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting0
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Visual Hallucinations of Multi-modal Large Language ModelsCode1
Measuring Multimodal Mathematical Reasoning with MATH-Vision DatasetCode2
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization0
LongWanjuan: Towards Systematic Measurement for Long Text QualityCode1
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness0
Diversity-Aware k-Maximum Inner Product Search Revisited0
Se^2: Sequential Example Selection for In-Context Learning0
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual LearningCode1
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