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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 2401–2410 of 9051 papers

TitleStatusHype
Global News Synchrony and Diversity During the Start of the COVID-19 PandemicCode0
Why does Knowledge Distillation Work? Rethink its Attention and Fidelity MechanismCode0
Modeling Caption Diversity in Contrastive Vision-Language PretrainingCode1
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design—0
Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network—0
Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imagingCode0
Soft Prompt Generation for Domain GeneralizationCode1
DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical DocumentsCode0
PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios—0
Integrating Present and Past in Unsupervised Continual LearningCode0
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