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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 27512760 of 9051 papers

TitleStatusHype
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits0
Diversity driven Query Rewriting in Search Advertising0
Ego-Downward and Ambient Video based Person Location Association0
Diversity in immunogenomics: the value and the challenge0
Diversity in Kemeny Rank Aggregation: A Parameterized Approach0
Diversity in Machine Learning0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models0
Diversity in Spectral Learning for Natural Language Parsing0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data0
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