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 871880 of 9051 papers

TitleStatusHype
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
A single-cell gene expression language modelCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
Contrastive Syn-to-Real GeneralizationCode1
Interpreting single-cell and spatial omics data using deep neural network training dynamicsCode1
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Invariant Feature Regularization for Fair Face RecognitionCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Show:102550
← PrevPage 88 of 906Next →

No leaderboard results yet.