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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 23312340 of 9051 papers

TitleStatusHype
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling0
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder0
Diversity-Achieving Slow-DropBlock Network for Person Re-Identification0
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code0
Coreference Resolution: Are the eliminated spans totally worthless?0
CodeFusion: A Pre-trained Diffusion Model for Code Generation0
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding0
Are Easy Data Easy (for K-Means)0
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?0
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms0
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