SOTAVerified

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 29412950 of 9051 papers

TitleStatusHype
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning0
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic RoomsCode2
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization0
Learning High-Quality and General-Purpose Phrase RepresentationsCode1
QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation0
Divide and not forget: Ensemble of selectively trained experts in Continual LearningCode0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection0
Generalized Face Liveness Detection via De-fake Face Generator0
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