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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 14111420 of 9051 papers

TitleStatusHype
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking TasksCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
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