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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

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Showing 1211–1220 of 9051 papers

TitleStatusHype
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
Contrastive Syn-to-Real GeneralizationCode1
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