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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 28712880 of 9051 papers

TitleStatusHype
Improved Generalization of Weight Space Networks via AugmentationsCode0
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing0
Avoiding an AI-imposed Taylor's Version of all music history0
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System0
Retrieval-Augmented Score Distillation for Text-to-3D GenerationCode2
Linguistic features for sentence difficulty prediction in ABSA0
"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with DefinitionCode0
Training-Free Consistent Text-to-Image GenerationCode2
Diversity Measurement and Subset Selection for Instruction Tuning Datasets0
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity0
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