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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 20212030 of 9051 papers

TitleStatusHype
Improving Generalization with Domain Convex GameCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and InclusionCode0
Multi-Objective Quality-Diversity for Crystal Structure PredictionCode0
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-trainingCode0
Multi-Objective Recommendation via Multivariate Policy LearningCode0
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention MechanismCode0
Cyclic image generation using chaotic dynamicsCode0
Hyperparameter Ensembles for Robustness and Uncertainty QuantificationCode0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
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