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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 191200 of 9051 papers

TitleStatusHype
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Diffusion Bridge Implicit ModelsCode2
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language ModelsCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
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