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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 20512060 of 9051 papers

TitleStatusHype
Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower ExtremitiesCode0
Hydra: Preserving Ensemble Diversity for Model DistillationCode0
Hyperparameter Auto-tuning in Self-Supervised Robotic LearningCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive RegularizationCode0
Coevolutionary Framework for Generalized Multimodal Multi-objective OptimizationCode0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
How well do you know your summarization datasets?Code0
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
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