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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 48714880 of 9051 papers

TitleStatusHype
Semi-supervised Semantic Segmentation with Mutual Knowledge DistillationCode1
Decentralized Collaborative Learning with Probabilistic Data Protection0
Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems0
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques0
Unsupervised Question Answering via Answer DiversifyingCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Tunable Hybrid Proposal Networks for the Open World0
Towards Confidence-aware Calibrated Recommendation0
Do diverse and inclusive workplaces benefit investors? An Empirical Analysis on Europe and the United States0
Automated Pruning of Polyculture Plants0
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