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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 4021–4030 of 9051 papers

TitleStatusHype
DMDD: A Large-Scale Dataset for Dataset Mentions Detection—0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Improving Recommendation System Serendipity Through Lexicase Selection—0
Augmented Message Passing Stein Variational Gradient Descent—0
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Learning In-context Learning for Named Entity RecognitionCode1
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques—0
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