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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 15511560 of 9051 papers

TitleStatusHype
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Few-Shot Object Detection via Synthetic Features with Optimal TransportCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data DiversityCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
Influence Selection for Active LearningCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
Instance-Optimal Compressed Sensing via Posterior SamplingCode1
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