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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 38013810 of 9051 papers

TitleStatusHype
FrameNet and Typology0
FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection0
A COLD Approach to Generating Optimal Samples0
Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers0
Free-Space Optical MISO Communications With an Array of Detectors0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation0
GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models0
Cost-Based Budget Active Learning for Deep Learning0
Diverse Neural Network Learns True Target Functions0
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