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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 481490 of 9051 papers

TitleStatusHype
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalsCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Voice Disorder Analysis: a Transformer-based ApproachCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
Concept-skill Transferability-based Data Selection for Large Vision-Language ModelsCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
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