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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 621630 of 9051 papers

TitleStatusHype
Quality-Diversity Generative Sampling for Learning with Synthetic DataCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
De novo Drug Design using Reinforcement Learning with Multiple GPT AgentsCode1
Q-SENN: Quantized Self-Explaining Neural NetworksCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Point Cloud Part Editing: Segmentation, Generation, Assembly, and SelectionCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data AugmentationCode1
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real ImagesCode1
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