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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 49114920 of 9051 papers

TitleStatusHype
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation0
Preserving Fine-Grain Feature Information in Classification via Entropic RegularizationCode0
DeepGen: Diverse Search Ad Generation and Real-Time Customization0
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning0
Mathematical Modeling Analysis and Optimization of Fungal Diversity Growth0
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding0
Evolutionary bagging for ensemble learningCode0
Rethinking the Evaluation of Unbiased Scene Graph Generation0
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence0
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