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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 45714580 of 9051 papers

TitleStatusHype
Deep Ensemble Policy Learning0
Deep Ensembles for Low-Data Transfer Learning0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-190
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks0
Deep Gait Tracking With Inertial Measurement Unit0
DeepGen: Diverse Search Ad Generation and Real-Time Customization0
Deep Generative Inpainting with Comparative Sample Augmentation0
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models0
Deep Generative Modelling of Human Reach-and-Place Action0
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering0
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