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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 2171–2180 of 9051 papers

TitleStatusHype
CityCraft: A Real Crafter for 3D City Generation—0
Diversified Batch Selection for Training AccelerationCode1
Bootstrapping Referring Multi-Object TrackingCode1
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
To Distill or Not to Distill? On the Robustness of Robust Knowledge DistillationCode0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
Polyhedral Conic Classifier for CTR Prediction—0
Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity—0
Improving Geo-diversity of Generated Images with Contextualized Vendi Score GuidanceCode1
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
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