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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 2121–2130 of 9051 papers

TitleStatusHype
Compositional Fine-Grained Low-Shot Learning—0
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation—0
A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems—0
Agricultural Economics and Innovation in the Inca Empire—0
Diffusion Deepfake—0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Diffusion-like recommendation with enhanced similarity of objects—0
Compositional diversity in visual concept learning—0
Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit regularization—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
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