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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 2201–2210 of 9051 papers

TitleStatusHype
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model—0
Communicate or Sense? AP Mode Selection in mmWave Cell-Free Massive MIMO-ISAC—0
Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling—0
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—0
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship—0
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity—0
A genomic map of the effects of linked selection in Drosophila—0
MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
Distribution Aware Metrics for Conditional Natural Language Generation—0
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