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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 48014810 of 9051 papers

TitleStatusHype
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from ImagesCode4
Counterfactual Explanations Using Optimization With Constraint LearningCode0
Prompting for a conversation: How to control a dialog model?0
Fine-Grained VR Sketching: Dataset and InsightsCode1
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training DynamicsCode1
Language Varieties of Italy: Technology Challenges and Opportunities0
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputCode1
Fairness on Synthetic Visual and Thermal Mask Images0
Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection0
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models0
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