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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 42914300 of 9051 papers

TitleStatusHype
Beyond Blur: A Fluid Perspective on Generative Diffusion Models0
An Empirical Analysis of Diversity in Argument Summarization0
Improving Data Efficiency via Curating LLM-Driven Rating Systems0
Improving Distribution Alignment with Diversity-based Sampling0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space0
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity0
Diffusion Deepfake0
A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images0
Diffusion-based Target Sampler for Unsupervised Domain Adaptation0
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