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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 5541–5550 of 9051 papers

TitleStatusHype
Synthetic data augmentation for robotic mobility aids to support blind and low vision people—0
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification—0
Synthetic Data Generation for Augmenting Small Samples—0
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method—0
Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Systematic Abductive Reasoning via Diverse Relation Representations in Vector-symbolic Architecture—0
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics—0
System Description for the CommonGen task with the POINTER model—0
Systems, Actors and Agents: Operation in a multicomponent environment—0
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