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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 811–820 of 9051 papers

TitleStatusHype
Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector SpacesCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Action detection using a neural network elucidates the genetics of mouse grooming behaviorCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Efficient OCR for Building a Diverse Digital HistoryCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
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