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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 201210 of 9051 papers

TitleStatusHype
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Learning Video Representations from Large Language ModelsCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
Benchmarking Representations for Speech, Music, and Acoustic EventsCode2
Magic Mirror: ID-Preserved Video Generation in Video Diffusion TransformersCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
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