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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 391400 of 9051 papers

TitleStatusHype
Data Augmentation using Pre-trained Transformer ModelsCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Dataset GrowthCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
Automating Rigid Origami DesignCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
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