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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 48114820 of 9051 papers

TitleStatusHype
RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations0
Learning Distinct and Representative Styles for Image CaptioningCode1
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances0
Neuro-evolutionary evidence for a universal fractal primate brain shapeCode1
Application of Liquid Rank Reputation System for Content Recommendation0
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic0
Distribution Aware Metrics for Conditional Natural Language Generation0
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation ApproachCode1
Non-Parallel Voice Conversion for ASR Augmentation0
vec2text with Round-Trip Translations0
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