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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 24812490 of 9051 papers

TitleStatusHype
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem0
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image AnalysisCode0
On the Scalability of GNNs for Molecular Graphs0
VBR: A Vision Benchmark in RomeCode2
Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection0
Incubating Text Classifiers Following User Instruction with Nothing but LLMCode0
Forcing Diffuse Distributions out of Language ModelsCode1
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language PromptingCode0
MAD Speech: Measures of Acoustic Diversity of Speech0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition0
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