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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 641650 of 9051 papers

TitleStatusHype
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Deep Ordinal Regression with Label DiversityCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
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