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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 28412850 of 9051 papers

TitleStatusHype
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary EvaluationCode0
Federated Stain Normalization for Computational PathologyCode0
Exploring the Role of Diversity in Example Selection for In-Context LearningCode0
Multilingual Lexical Simplification via Paraphrase GenerationCode0
Multilingual Neural Machine Translation with Knowledge DistillationCode0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Deep Learning Models for Atypical Serotonergic Cells RecognitionCode0
Exploring the Evolution of GANs through Quality DiversityCode0
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