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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 751760 of 9051 papers

TitleStatusHype
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Exclusive Hierarchical Decoding for Deep Keyphrase GenerationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active ExplorationCode1
Exploring Design of Multi-Agent LLM Dialogues for Research IdeationCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
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