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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 961970 of 9051 papers

TitleStatusHype
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial LearningCode1
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM ImagesCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal AnglesCode1
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment DatabaseCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
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