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 561570 of 9051 papers

TitleStatusHype
MapQA: Open-domain Geospatial Question Answering on Map Data0
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories0
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast AsiaCode0
AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis0
Process-Supervised LLM Recommenders via Flow-guided TuningCode1
PER-DPP Sampling Framework and Its Application in Path Planning0
Color Alignment in Diffusion0
ExGes: Expressive Human Motion Retrieval and Modulation for Audio-Driven Gesture Synthesis0
Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection0
Instance-wise Supervision-level Optimization in Active LearningCode0
Show:102550
← PrevPage 57 of 906Next →

No leaderboard results yet.