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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 841850 of 9051 papers

TitleStatusHype
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
AI-generated text boundary detection with RoFTCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
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