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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 871880 of 9051 papers

TitleStatusHype
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic DataCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Neural Video Compression with Diverse ContextsCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
Tailoring Language Generation Models under Total Variation DistanceCode1
Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis DetectionCode1
Diverse Policy Optimization for Structured Action SpaceCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
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