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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 17811790 of 9051 papers

TitleStatusHype
Improving Transferability of Adversarial Examples with Input DiversityCode0
Increasing Entropy to Boost Policy Gradient Performance on Personalization TasksCode0
Active Learning for Regression Using Greedy SamplingCode0
Consistency-based anomaly detection with adaptive multiple-hypotheses predictionsCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal StudyCode0
A Simple Method for Commonsense ReasoningCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
A Hierarchical Deep Learning Approach for Minority Instrument DetectionCode0
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