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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 14611470 of 9051 papers

TitleStatusHype
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Adversarial Parametric Pose PriorCode1
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input ModalitiesCode1
Implicit Neural Representations for Variable Length Human Motion GenerationCode1
Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identificationCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task LearningCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
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