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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 3711–3720 of 9051 papers

TitleStatusHype
Exploring Implicit Sentiment Evoked by Fine-grained News Events—0
Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion—0
Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media—0
Finding Support Examples for In-Context Learning—0
Fine-grained Activities of People Worldwide—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems—0
Exploring Global Diversity and Local Context for Video Summarization—0
Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation—0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
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