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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 23112320 of 9051 papers

TitleStatusHype
Topology-Preserved Human Reconstruction with DetailsCode0
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary AlgorithmsCode0
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component AnalysisCode0
Brain Tumor Synthetic Data Generation with Adaptive StyleGANsCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Generating Neural Networks with Neural NetworksCode0
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generating Language Corrections for Teaching Physical Control TasksCode0
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning ModelsCode0
Generating Natural Language Adversarial ExamplesCode0
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