This paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric ...
Jan 24, 2022 · First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then ...
This article proposes a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images.
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This repository contains several scripts of Uncertainty-Aware Neural Network Trainining. We apply codes to several datasets and upload codes in Jupyter ...
In this article, we study quantification of model uncertainty based on Monte-Carlo Dropout (MC Dropout) neural networks in prediction models developed from CPS ...
Oct 12, 2023 · Aleatory-aware deep uncertainty quantifica- tion for transfer learning. Computers in Biology and Medicine 143, 105246. Kabir, H.D., Khosravi ...
Feb 25, 2023 · This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs.
May 6, 2024 · Retraction Note: Robust adversarial uncertainty quantification for deep learning fine-tuning. Authors: Usman Ahmed.
Mar 29, 2024 · In this paper, we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks, called SAEU.
Mar 1, 2024 · We demonstrate, for the first time, that it is possible to transfer calibrated uncertainty information along with knowledge from upstream tasks ...