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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 8926–8950 of 9051 papers

TitleStatusHype
A new efficient Matching method for web services substitution—0
Characterizing the dynamics of rubella relative to measles: the role of stochasticity—0
Unsupervised Segmentation of Multispectral Images with Cellular Automata—0
Annotating Cognates and Etymological Origin in Turkic Languages—0
Analysing domain shift factors between videos and images for object detectionCode0
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks—0
Automatic Discovery and Optimization of Parts for Image Classification—0
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection—0
Sparsity and adaptivity for the blind separation of partially correlated sourcesCode0
Self-Paced Learning with Diversity—0
Learning Mixtures of Submodular Functions for Image Collection Summarization—0
Learning Deep Features for Scene Recognition using Places Database—0
Diversifying Sparsity Using Variational Determinantal Point Processes—0
Diversity Handling In Evolutionary Landscape—0
A unified view of generative models for networks: models, methods, opportunities, and challenges—0
DUM: Diversity-Weighted Utility Maximization for Recommendations—0
Large-Margin Determinantal Point Processes—0
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets—0
Expectation-Maximization for Learning Determinantal Point Processes—0
Entropy of Overcomplete Kernel Dictionaries—0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
POLYGLOT-NER: Massive Multilingual Named Entity Recognition—0
Replicate immunosequencing as a robust probe of B cell repertoire diversity—0
Distance Shrinkage and Euclidean Embedding via Regularized Kernel Estimation—0
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection—0
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