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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 56115620 of 9051 papers

TitleStatusHype
Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform0
Texturize a GAN Using a Single Image0
TGermaCorp -- A (Digital) Humanities Resource for (Computational) Linguistics0
The 1st Workshop on Human-Centered Recommender Systems0
The 2020s Political Economy of Machine Translation0
The 2nd Place Solution from the 3D Semantic Segmentation Track in the 2024 Waymo Open Dataset Challenge0
The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models0
The ACQDIV Database: Min(d)ing the Ambient Language0
The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers0
The Amazing World of Neural Language Generation0
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