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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 661670 of 9051 papers

TitleStatusHype
Controllable Video Captioning with an Exemplar SentenceCode1
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose EstimationCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Differentiable Quality DiversityCode1
Differential Evolution with Reversible Linear TransformationsCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
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