13 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.CV2024★ 1 cited
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
Changbin Li, Kangshuo Li, Yuzhe Ou +5
Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes…
cs.LG2020
Multidimensional Uncertainty-Aware Evidential Neural Networks
Yibo Hu, Yuzhe Ou, Xujiang Zhao +2
Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However,…
cs.LG2019★ 13 cited
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
Xujiang Zhao, Yuzhe Ou, Lance Kaplan +2
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of…