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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 12011210 of 9051 papers

TitleStatusHype
Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin TonesCode1
Input-Aware Dynamic Backdoor AttackCode1
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiersCode1
GenDexGrasp: Generalizable Dexterous GraspingCode1
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
General and Task-Oriented Video SegmentationCode1
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