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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 18711880 of 9051 papers

TitleStatusHype
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Improving Contextualized Topic Models with Negative SamplingCode0
Improving Diversity of Commonsense Generation by Large Language Models via In-Context LearningCode0
Competition and Diversity in Generative AICode0
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output CodesCode0
Improved Generation of Synthetic Imaging Data Using Feature-Aligned DiffusionCode0
Comparison of Diverse Decoding Methods from Conditional Language ModelsCode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
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