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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 441450 of 9051 papers

TitleStatusHype
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative GenerationCode1
DebateQA: Evaluating Question Answering on Debatable KnowledgeCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Few-shot Defect Image Generation based on Consistency ModelingCode1
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
WAS: Dataset and Methods for Artistic Text SegmentationCode1
Monocular Human-Object Reconstruction in the WildCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
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