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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 3371–3380 of 9051 papers

TitleStatusHype
Fundamental Limits of Membership Inference Attacks on Machine Learning Models—0
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models—0
Benchmarking and Improving Text-to-SQL Generation under AmbiguityCode0
Knowledge Graph Context-Enhanced Diversified RecommendationCode0
PSGText: Stroke-Guided Scene Text Editing with PSP Module—0
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion ModelsCode1
Quality-Diversity through AI Feedback—0
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
CLAIR: Evaluating Image Captions with Large Language Models—0
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