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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 15111520 of 9051 papers

TitleStatusHype
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain AdaptationCode0
Text-driven Human Motion Generation with Motion Masked Diffusion Model0
When Molecular GAN Meets Byte-Pair Encoding0
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding0
Investigating the Impact of Text Summarization on Topic Modeling0
Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets0
Conditional Image Synthesis with Diffusion Models: A SurveyCode2
On the Power of Decision Trees in Auto-Regressive Language Modeling0
Diverse Code Query Learning for Speech-Driven Facial Animation0
Reducing Diversity to Generate Hierarchical Archetypes0
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