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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

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Showing 5561–5570 of 9051 papers

TitleStatusHype
TARDiS : Text Augmentation for Refining Diversity and Separability—0
Target Conditioning for One-to-Many Generation—0
Target Detection in OFDM-ISAC Systems: A Multipath Exploitation Approach—0
Targeted AMP generation through controlled diffusion with efficient embeddings—0
Targeted Augmentation for Low-Resource Event Extraction—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
TA-Student VQA: Multi-Agents Training by Self-Questioning—0
T cell receptor binding prediction: A machine learning revolution—0
Teaching to Teach by Structured Dark Knowledge—0
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