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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 33513360 of 9051 papers

TitleStatusHype
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
Invariant Feature Regularization for Fair Face RecognitionCode1
DPP-TTS: Diversifying prosodic features of speech via determinantal point processes0
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diverse Priors for Deep Reinforcement Learning0
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic FieldsCode0
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Iteratively Learn Diverse Strategies with State Distance Information0
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
Semantic and Expressive Variation in Image Captions Across Languages0
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