Search Results for author: Moloud Abdar

Found 8 papers, 4 papers with code

Improving MC-Dropout Uncertainty Estimates with Calibration Error-based Optimization

no code implementations7 Oct 2021 Afshar Shamsi, Hamzeh Asgharnezhad, Moloud Abdar, AmirReza Tajally, Abbas Khosravi, Saeid Nahavandi, Henry Leung

Uncertainty quantification of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result.

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

1 code implementation24 Aug 2021 Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber, Moloud Abdar, U Rajendra Acharya, Abbas Khosravi, Saeid Nahavandi

It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.

Breast Cancer Histology Image Classification Classification +1

UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

1 code implementation18 May 2021 Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhri Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi

Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.

Computed Tomography (CT)

A Review of Generalized Zero-Shot Learning Methods

1 code implementation17 Nov 2020 Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim, Xi-Zhao Wang, Q. M. Jonathan Wu

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning.

Generalized Zero-Shot Learning

SpinalNet: Deep Neural Network with Gradual Input

2 code implementations arXiv 2020 H M Dipu Kabir, Moloud Abdar, Seyed Mohammad Jafar Jalali, Abbas Khosravi, Amir F. Atiya, Saeid Nahavandi, Dipti Srinivasan

Traditional learning with ImageNet pre-trained initial weights and SpinalNet classification layers provided the SOTA performance on STL-10, Fruits 360, Bird225, and Caltech-101 datasets.

Fine-Grained Image Classification Transfer Learning

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

no code implementations17 Sep 2019 Thang Doan, Bogdan Mazoure, Moloud Abdar, Audrey Durand, Joelle Pineau, R. Devon Hjelm

Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions.

Continuous Control reinforcement-learning

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