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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 3601–3610 of 9051 papers

TitleStatusHype
Content-Diverse Comparisons improve IQA—0
Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES—0
Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering—0
An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP—0
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity—0
Fast inverse lithography based on a model-driven block stacking convolutional neural network—0
Fast Re-Optimization via Structural Diversity—0
Diverse Yet Efficient Retrieval using Hash Functions—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
GaitGCI: Generative Counterfactual Intervention for Gait Recognition—0
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