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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 37013710 of 9051 papers

TitleStatusHype
Angle Sensitive Pixels for Lensless Imaging on Spherical Sensors0
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data0
Filter-based Discriminative Autoencoders for Children Speech Recognition0
Filter Bubble or Homogenization? Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns0
Diverse Projection Ensembles for Distributional Reinforcement Learning0
Find and Focus: Retrieve and Localize Video Events with Natural Language Queries0
ADLM -- stega: A Universal Adaptive Token Selection Algorithm for Improving Steganographic Text Quality via Information Entropy0
A Tailored NSGA-III Instantiation for Flexible Job Shop Scheduling0
Finding Near-Optimal Portfolios With Quality-Diversity0
Generating Discriminative Object Proposals via Submodular Ranking0
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