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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

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Showing 5141–5150 of 9051 papers

TitleStatusHype
Scaling up Image Segmentation across Data and Tasks—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
SceneGen: Learning to Generate Realistic Traffic Scenes—0
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach—0
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
SceneX: Procedural Controllable Large-scale Scene Generation—0
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness—0
Why scholars are diagramming neural network models—0
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