F
Farhad Pourpanah
Researcher at Shenzhen University
Publications - 33
Citations - 1720
Farhad Pourpanah is an academic researcher from Shenzhen University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 14, co-authored 23 publications receiving 504 citations. Previous affiliations of Farhad Pourpanah include Southern University of Science and Technology & Universiti Sains Malaysia.
Papers
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Journal ArticleDOI
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
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Design, Implementation, and Evaluation of a Neural-Network-Based Quadcopter UAV System
TL;DR: A quadcopter unmanned aerial vehicle (UAV) system based on neural-network enhanced dynamic inversion control is proposed for multiple real-world application scenarios and can achieve much higher accuracy in attitude and trajectory control than that achieved by conventional proportional-integral derivative based control systems under varying flight conditions.
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A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TL;DR: Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of uncertainties during both optimization and decision making processes as mentioned in this paper, and have been applied to solve a variety of real-world problems in science and engineering Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification.
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Intuitionistic Fuzzy Twin Support Vector Machines
TL;DR: This paper presents an intuitionistic FTSVM (IFTSVM) that combines the idea of intuitionistic fuzzy number with twin support vector machine (TSVM).