no code implementations • 7 Oct 2021 • Afshar Shamsi, Hamzeh Asgharnezhad, AmirReza Tajally, Saeid Nahavandi, Henry Leung
Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.
no code implementations • 28 Jul 2021 • Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi, Abbas Khosravi, Miadreza Shafie-khah, Saeid Nahavandi, Joao P. S. Catalao
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models.
no code implementations • 24 Jul 2021 • Maryam Habibpour, Hassan Gharoun, AmirReza Tajally, Afshar Shamsi, Hamzeh Asgharnezhad, Abbas Khosravi, Saeid Nahavandi
Secondly, to achieve a reliable classification and to measure epistemic uncertainty, we employ an uncertainty quantification (UQ) technique (ensemble of MLP models) using features extracted from four pre-trained CNNs.
no code implementations • 19 Jul 2021 • Donya Khaledyan, AmirReza Tajally, Ali Sarkhosh, Afshar Shamsi, Hamzeh Asgharnezhad, Abbas Khosravi, Saeid Nahavandi
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities.