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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 48914900 of 9051 papers

TitleStatusHype
Quality Diversity Evolutionary Learning of Decision Trees0
Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork0
Road detection via a dual-task network based on cross-layer graph fusion modules0
TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual EnvironmentsCode1
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose EstimationCode1
A User-Centered Investigation of Personal Music Tours0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation0
A Case for Rejection in Low Resource ML DeploymentCode1
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