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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 32313240 of 9051 papers

TitleStatusHype
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context0
Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading0
Ensembles of GANs for synthetic training data generation0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention0
Bregman Centroid Guided Cross-Entropy Method0
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence0
Ensembles of Random SHAPs0
3D Neural Field Generation using Triplane Diffusion0
Exploring the Quality of GAN Generated Images for Person Re-Identification0
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