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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 381390 of 9051 papers

TitleStatusHype
AutoMix: Automatically Mixing Language ModelsCode1
Dataset Factorization for CondensationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Dataset GrowthCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
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