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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

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Showing 4701–4710 of 9051 papers

TitleStatusHype
Differential learning kinetics govern the transition from memorization to generalization during in-context learning—0
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Prague Dependency Treebank -- Consolidated 1.0—0
Prb-GAN: A Probabilistic Framework for GAN Modelling—0
Predator-prey survival pressure is sufficient to evolve swarming behaviors—0
Predicting Adversarial Examples with High Confidence—0
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation—0
Predicting conversion of mild cognitive impairment to Alzheimer's disease—0
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)—0
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