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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3841–3850 of 9051 papers

TitleStatusHype
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Comprehensive Annotation of Multiword Expressions in a Social Web Corpus—0
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling—0
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images—0
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection—0
Exploiting Angular Multiplexing for Polarization-diversity in Off-axis Digital Holography—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Show:102550
← PrevPage 385 of 906Next →

No leaderboard results yet.