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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 12711280 of 9051 papers

TitleStatusHype
Generative Adversarial Graph Convolutional Networks for Human Action SynthesisCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
Generative Meta-Learning Robust Quality-Diversity PortfolioCode1
Generative Modeling in Structural-Hankel Domain for Color Image InpaintingCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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