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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 21812190 of 9051 papers

TitleStatusHype
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
GridDehazeNet: Attention-Based Multi-Scale Network for Image DehazingCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup SegmentationCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
Gradient Estimators for Implicit ModelsCode0
Adversarial Multi-lingual Neural Relation ExtractionCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
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