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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 951960 of 9051 papers

TitleStatusHype
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational ModelCode1
Style Transfer as Data Augmentation: A Case Study on Named Entity RecognitionCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Near-Optimal Multi-Agent Learning for Safe Coverage ControlCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
What Makes Graph Neural Networks Miscalibrated?Code1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Semi-supervised Semantic Segmentation with Prototype-based Consistency RegularizationCode1
SCAM! Transferring humans between images with Semantic Cross Attention ModulationCode1
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