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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 171180 of 9051 papers

TitleStatusHype
Diffusion Bridge Implicit ModelsCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
EvalGIM: A Library for Evaluating Generative Image ModelsCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
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