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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 18911900 of 9051 papers

TitleStatusHype
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Artificial Immune System of Secure Face Recognition Against Adversarial AttacksCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistenceCode0
ABD-Net: Attentive but Diverse Person Re-IdentificationCode0
Improving Contextualized Topic Models with Negative SamplingCode0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Attesting Distributional Properties of Training Data for Machine LearningCode0
Correlation and Navigation in the Vocabulary Key Representation Space of Language ModelsCode0
Importance Weighted Expectation-Maximization for Protein Sequence DesignCode0
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