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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 34813490 of 9051 papers

TitleStatusHype
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
Towards a Unified Framework for Adaptable Problematic Content Detection via Continual LearningCode0
Post-Training Overfitting Mitigation in DNN Classifiers0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling0
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy MaximizationCode0
SJTU-TMQA: A quality assessment database for static mesh with texture map0
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V0
Adaptive Priority Mechanisms0
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