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 22112220 of 9051 papers

TitleStatusHype
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Cascading CMA-ES Instances for Generating Input-diverse Solution BatchesCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
A Comprehensive Evaluation on Event Reasoning of Large Language ModelsCode0
Gradient Estimators for Implicit ModelsCode0
GPoeT-2: A GPT-2 Based Poem GeneratorCode0
GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and DovesCode0
Show:102550
← PrevPage 222 of 906Next →

No leaderboard results yet.