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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 12411250 of 9051 papers

TitleStatusHype
FHDe²Net: Full High Definition Demoireing NetworkCode1
Bias Loss for Mobile Neural NetworksCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
Amortizing intractable inference in large language modelsCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
AutoMix: Automatically Mixing Language ModelsCode1
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