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 18311840 of 9051 papers

TitleStatusHype
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
Contributing Dimension Structure of Deep Feature for Coreset SelectionCode0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving Generalization with Domain Convex GameCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
Show:102550
← PrevPage 184 of 906Next →

No leaderboard results yet.