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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

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Showing 6601–6625 of 9051 papers

TitleStatusHype
Unsupervised Estimation of Ensemble Accuracy—0
Practical Insights of Repairing Model Problems on Image Classification—0
Taxonomy of Machine Learning Safety: A Survey and Primer—0
AnyFace: Free-style Text-to-Face Synthesis and Manipulation—0
Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification—0
Pragmatically Appropriate Diversity for Dialogue Evaluation—0
Prague Dependency Treebank - Consolidated 1.0—0
Prague Dependency Treebank -- Consolidated 1.0—0
Prb-GAN: A Probabilistic Framework for GAN Modelling—0
AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea—0
Predator-prey survival pressure is sufficient to evolve swarming behaviors—0
Predicting Adversarial Examples with High Confidence—0
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation—0
Unity in Diversity: Video Editing via Gradient-Latent Purification—0
Predicting conversion of mild cognitive impairment to Alzheimer's disease—0
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)—0
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques—0
Predicting retrosynthetic pathways using a combined linguistic model and hyper-graph exploration strategy—0
Predicting scalar diversity with context-driven uncertainty over alternatives—0
Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach—0
Predicting Success in Goal-Driven Human-Human Dialogues—0
Predicting the diversity of early epidemic spread on networks—0
Universal and data-adaptive algorithms for model selection in linear contextual bandits—0
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting—0
Prediction of viral spillover risk based on the mass action principle—0
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