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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 23212330 of 9051 papers

TitleStatusHype
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning ModelsCode0
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
BooVAE: Boosting Approach for Continual Learning of VAECode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single ImageCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing DataCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
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