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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 34913500 of 9051 papers

TitleStatusHype
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
Beauty beacon: correlated strategies for the Fisher runaway process0
Structure Invariant Transformation for better Adversarial TransferabilityCode1
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender SystemsCode0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch0
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationCode1
Exploring Robot Morphology Spaces through Breadth-First Search and Random Query0
Convolutional autoencoder-based multimodal one-class classification0
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience0
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