2.7k citations · 3.5k across the 17 of their papers we have counts for
6 papers · 1 filter
Few-Shot Inspired Generative Zero-Shot Learning
Md Shakil Ahamed Shohag, Q. M. Jonathan Wu, Farhad Pourpanah
Generative zero-shot learning (ZSL) methods typically synthesize visual features for unseen classes using predefined semantic attributes, followed by training a fully supervised cl…
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani +2
In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data hete…
Methods for Class-Imbalanced Learning with Support Vector Machines: A Review and an Empirical Evaluation
Salim Rezvani, Farhad Pourpanah, Chee Peng Lim +1
This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants…
Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients
Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany +2
We address the problem of federated domain generalization in an unsupervised setting for the first time. We first theoretically establish a connection between domain shift and alig…
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications
Xinlei Zhou, Han Liu, Farhad Pourpanah +2
Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient k…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…