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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 37513760 of 9051 papers

TitleStatusHype
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Rumor Detection with Diverse Counterfactual EvidenceCode1
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow0
Towards Task Sampler Learning for Meta-LearningCode1
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML0
Active Learning for Object Detection with Non-Redundant Informative Sampling0
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models0
Towards Viewpoint-Invariant Visual Recognition via Adversarial TrainingCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
A Hybrid Genetic Algorithm for the min-max Multiple Traveling Salesman Problem0
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