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 441450 of 9051 papers

TitleStatusHype
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale AmbiguitiesCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
AlphaFold Distillation for Protein DesignCode1
Show:102550
← PrevPage 45 of 906Next →

No leaderboard results yet.