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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 29112920 of 9051 papers

TitleStatusHype
Advances in Multi-turn Dialogue Comprehension: A Survey0
StyLitGAN: Prompting StyleGAN to Produce New Illumination Conditions0
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation0
Dropping diversity of products of large US firms: Models and measures0
Enrich the content of the image Using Context-Aware Copy Paste0
Diversity-aware Web APIs Recommendation with Compatibility Guarantee0
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling0
DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network0
BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation0
Diversity-aware social robots meet people: beyond context-aware embodied AI0
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