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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 15211530 of 9051 papers

TitleStatusHype
Self-Distillation as Instance-Specific Label SmoothingCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Self-supervised Assisted Active Learning for Skin Lesion SegmentationCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
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