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 361370 of 9051 papers

TitleStatusHype
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
AutoMix: Automatically Mixing Language ModelsCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Dan: Deep attention neural network for news recommendationCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Show:102550
← PrevPage 37 of 906Next →

No leaderboard results yet.