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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 3631–3640 of 9051 papers

TitleStatusHype
Diverse Video Captioning Through Latent Variable Expansion—0
Feature Selection Based on Term Frequency and T-Test for Text Categorization—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
Feature Selection for Classification under Anonymity Constraint—0
Feature Statistics Guided Efficient Filter Pruning—0
Feature Weighted Non-negative Matrix Factorization—0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment—0
FedAvgen: Metadata for Model Aggregation In Communication Systems—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
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