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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 41214130 of 9051 papers

TitleStatusHype
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning0
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic ServicesCode0
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
SP-BatikGAN: An Efficient Generative Adversarial Network for Symmetric Pattern Generation0
ReelFramer: Human-AI Co-Creation for News-to-Video Translation0
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
Analysing Equilibrium States for Population Diversity0
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image ModelsCode0
Dual Stage Stylization Modulation for Domain Generalized Semantic Segmentation0
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