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 19011910 of 9051 papers

TitleStatusHype
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance0
Semantic-Aware Representation of Multi-Modal Data for Data Ingress: A Literature Review0
On Diversity in Discriminative Neural Networks0
Conditional Quantile Estimation for Uncertain Watch Time in Short-Video RecommendationCode0
The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation0
RIS-Assisted High Resolution Radar Sensing0
VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification BenchmarkCode5
Better RAG using Relevant Information GainCode0
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
Show:102550
← PrevPage 191 of 906Next →

No leaderboard results yet.