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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 7251–7275 of 9051 papers

TitleStatusHype
Open Generative Large Language Models for Galician—0
Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark—0
Open Language Data Initiative: Advancing Low-Resource Machine Translation for Karakalpak—0
Open Loop Hyperparameter Optimization and Determinantal Point Processes—0
Open Problems in Human Trait Genetics—0
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Open-Vocabulary Object Detection via Neighboring Region Attention Alignment—0
Open-World Evaluation for Retrieving Diverse Perspectives—0
Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces—0
Operational Neural Networks—0
Operational vs Convolutional Neural Networks for Image Denoising—0
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research—0
Opposition Based ElectromagnetismLike for Global Optimization—0
Opposition based Ensemble Micro Differential Evolution—0
Optical Field Recovery in Jones Space—0
Optical Wireless cabin communication system—0
Optimal Budgeted Rejection Sampling for Generative Models—0
Optimal compound downselection to promote diversity and parallel chemistry—0
Optimal Execution Using Reinforcement Learning—0
Optimal navigability of weighted human brain connectomes in physical space—0
Optimal Selective Attention in Reactive Agents—0
Optimal Transport Based Generative Autoencoders—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
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