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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 31213130 of 9051 papers

TitleStatusHype
A hybrid ensemble method with negative correlation learning for regressionCode0
Ensemble Distribution DistillationCode0
Ensemble Pruning based on Objection Maximization with a General Distributed FrameworkCode0
HeteroMorpheus: Universal Control Based on Morphological Heterogeneity ModelingCode0
Improved Paraphrase Generation via Controllable Latent DiffusionCode0
Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric MaterialsCode0
Enhancing Relation Extraction Using Syntactic Indicators and Sentential ContextsCode0
Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial AttacksCode0
Enhancing Robustness of AI Offensive Code Generators via Data AugmentationCode0
Enhancing Image Generation Fidelity via Progressive PromptsCode0
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