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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 30713080 of 9051 papers

TitleStatusHype
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing dataCode0
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
UINav: A Practical Approach to Train On-Device Automation Agents0
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data AugmentationCode1
Unraveling Batch Normalization for Realistic Test-Time AdaptationCode0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning0
DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning0
CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels0
ArchiGuesser -- AI Art Architecture Educational GameCode0
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