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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 471480 of 9051 papers

TitleStatusHype
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source DataCode1
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model BiasCode1
Calliar: An Online Handwritten Dataset for Arabic CalligraphyCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Diverse Policy Optimization for Structured Action SpaceCode1
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