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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 30713080 of 9051 papers

TitleStatusHype
Data-efficient Neuroevolution with Kernel-Based Surrogate ModelsCode0
Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)Code0
EquiBoost: An Equivariant Boosting Approach to Molecular Conformation GenerationCode0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
Error Diversity Matters: An Error-Resistant Ensemble Method for Unsupervised Dependency ParsingCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data SetsCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
EPiC: Ensemble of Partial Point Clouds for Robust ClassificationCode0
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