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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 521530 of 9051 papers

TitleStatusHype
Personalize Anything for Free with Diffusion Transformer0
Tailor: An Integrated Text-Driven CG-Ready Human and Garment Generation System0
RONA: Pragmatically Diverse Image Captioning with Coherence RelationsCode0
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning0
Make Optimization Once and for All with Fine-grained Guidance0
MUSS: Multilevel Subset Selection for Relevance and Diversity0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models0
Beamforming for Movable and Rotatable Antenna Enabled Multi-User Communications0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
Climate land use and other drivers impacts on island ecosystem services: a global review0
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