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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 18611870 of 9051 papers

TitleStatusHype
Self-Supervision Improves Diffusion Models for Tabular Data ImputationCode1
PEFT-U: Parameter-Efficient Fine-Tuning for User PersonalizationCode0
Multipath Identification and Mitigation with FDA-MIMO Radar0
XS-VID: An Extremely Small Video Object Detection Dataset0
The FIGNEWS Shared Task on News Media Narratives0
Image Segmentation via Divisive Normalization: dealing with environmental diversity0
Network Inversion of Convolutional Neural Nets0
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
Diversity in Choice as Majorization0
Pose Estimation from Camera Images for Underwater Inspection0
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