5 citations · 7 across the 4 of their papers we have counts for
4 papers
Evidential time-to-event prediction with calibrated uncertainty quantification
Ling Huang, Yucheng Xing, Swapnil Mishra +2
Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the…
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers
Ling Huang, Yucheng Xing, Thierry Denoeux +1
We introduce an evidential model for time-to-event prediction with censored data. In this model, uncertainty on event time is quantified by Gaussian random fuzzy numbers, a newly i…
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Ling Huang, Su Ruan, Pierre Decazes +1
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases base…
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Zied Bouraoui, Antoine Cornuéjols, Thierry Denœux +9
This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been deve…