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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 38813890 of 9051 papers

TitleStatusHype
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models0
Evaluating and Incentivizing Diverse Data Contributions in Collaborative Learning0
Towards Understanding the Interplay of Generative Artificial Intelligence and the InternetCode0
Enhancing Robustness of AI Offensive Code Generators via Data AugmentationCode0
Stochastic Multi-Person 3D Motion ForecastingCode1
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces0
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification0
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsCode1
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion modelsCode2
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