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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 151160 of 9051 papers

TitleStatusHype
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language ModelsCode2
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone DesignCode2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Can Go AIs be adversarially robust?Code2
Scaling Efficient Masked Image Modeling on Large Remote Sensing DatasetCode2
STAR: Scale-wise Text-to-image generation via Auto-Regressive representationsCode2
SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian LanguagesCode2
Consistency-diversity-realism Pareto fronts of conditional image generative modelsCode2
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow UnderstandingCode2
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