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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 1876–1900 of 9051 papers

TitleStatusHype
Cooperative Intergroup Mating Can Overcome Ethnocentrism in Diverse Populations—0
Navigating Text-to-Image Generative Bias across Indic Languages—0
Deep Negative Correlation Classification—0
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population—0
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds—0
Coordinated Exploration in Concurrent Reinforcement Learning—0
Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems—0
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Deep Learning of Determinantal Point Processes via Proper Spectral Sub-gradient—0
CORD: Generalizable Cooperation via Role Diversity—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Considerations for Ethical Speech Recognition Datasets—0
Coreference in Spoken vs. Written Texts: a Corpus-based Analysis—0
Coreset Selection for Object Detection—0
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning—0
Corporate Social Responsibility and Corporate Governance: A cognitive approach—0
Corpus-based Content Construction—0
Corpus COFLA: A research corpus for the Computational study of Flamenco Music—0
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis—0
Corpus Development of Kiswahili Speech Recognition Test and Evaluation sets, Preemptively Mitigating Demographic Bias Through Collaboration with Linguists—0
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment—0
Correlated Errors in Large Language Models—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model—0
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