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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 531540 of 9051 papers

TitleStatusHype
Treatment Effect Estimation for User Interest Exploration on Recommender SystemsCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable PromptCode1
Pedestrian Attribute Recognition as Label-balanced Multi-label LearningCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Navigating Chemical Space with Latent FlowsCode1
Towards Geographic Inclusion in the Evaluation of Text-to-Image ModelsCode1
Argumentative Large Language Models for Explainable and Contestable Claim VerificationCode1
Inherent Trade-Offs between Diversity and Stability in Multi-Task BenchmarksCode1
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business DocumentsCode1
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