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 42814290 of 9051 papers

TitleStatusHype
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing SymmetriesCode0
Effective Visualization and Analysis of Recommender Systems0
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization0
Neural Video Compression with Diverse ContextsCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
Can We Use Diffusion Probabilistic Models for 3D Motion Prediction?0
Show:102550
← PrevPage 429 of 906Next →

No leaderboard results yet.