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 47714780 of 9051 papers

TitleStatusHype
Analyzing the Dialect Diversity in Multi-document SummariesCode0
Accuracy meets Diversity in a News Recommender System0
Can Data Diversity Enhance Learning Generalization?0
Evaluating Diversity of Multiword Expressions in Annotated Text0
Towards Summarizing Healthcare Questions in Low-Resource Setting0
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference0
Social and environmental impact of recent developments in machine learning on biology and chemistry researchCode0
School closures and educational path: how the Covid-19 pandemic affected transitions to college0
Towards complete representation of bacterial contents in metagenomic samples0
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm0
Show:102550
← PrevPage 478 of 906Next →

No leaderboard results yet.