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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 4561–4570 of 9051 papers

TitleStatusHype
Deep Active Learning in the Presence of Label Noise: A Survey—0
Deep Architectures and Ensembles for Semantic Video Classification—0
Deep Billboards towards Lossless Real2Sim in Virtual Reality—0
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images—0
Deep Concept Identification for Generative Design—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
Deep Delay Loop Reservoir Computing for Specific Emitter Identification—0
Deep Determinantal Point Processes—0
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
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