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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 10811090 of 9051 papers

TitleStatusHype
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
Adaptively Sparse TransformersCode1
BLEU might be Guilty but References are not InnocentCode1
Biological Sequence Design with GFlowNetsCode1
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