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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

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Showing 511–520 of 9051 papers

TitleStatusHype
Learning diverse attacks on large language models for robust red-teaming and safety tuningCode1
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
Dataset GrowthCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time SeriesCode1
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
Learning to Transform Dynamically for Better Adversarial TransferabilityCode1
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