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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 5576–5600 of 9051 papers

TitleStatusHype
Statistics of the Effective Massive MIMO Channel in Correlated Rician Fading—0
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning—0
Learning Semantic-Aligned Feature Representation for Text-based Person SearchCode1
Makeup216: Logo Recognition with Adversarial Attention Representations—0
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System—0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
The Past as a Stochastic Process—0
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary DetectionCode1
Multimodal Conditional Image Synthesis with Product-of-Experts GANs—0
Guardian of the Ensembles: Introducing Pairwise Adversarially Robust Loss for Resisting Adversarial Attacks in DNN EnsemblesCode0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Adversarial Parametric Pose PriorCode1
A systematic approach to random data augmentation on graph neural networks—0
Boosting Deep Ensemble Performance with Hierarchical PruningCode0
VizExtract: Automatic Relation Extraction from Data Visualizations—0
CG-NeRF: Conditional Generative Neural Radiance Fields—0
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone DeckbuildingCode0
Saliency Diversified Deep Ensemble for Robustness to Adversaries—0
Dataset Geography: Mapping Language Data to Language UsersCode0
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints—0
HIVE: Evaluating the Human Interpretability of Visual ExplanationsCode1
NL-Augmenter: A Framework for Task-Sensitive Natural Language AugmentationCode1
Make It Move: Controllable Image-to-Video Generation with Text DescriptionsCode1
Texture Reformer: Towards Fast and Universal Interactive Texture TransferCode1
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates—0
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