6 citations · 6 across the 6 of their papers we have counts for
6 papers
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
Mohammad Azizmalayeri, Reza Abbasi, Amir Hosein Haji Mohammad rezaie +4
Deep neural networks have exhibited remarkable performance in various domains. However, the reliance of these models on spurious features has raised concerns about their reliabilit…
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods
Mahdi Salmani, Alireza Dehghanpour Farashah, Mohammad Azizmalayeri +4
Despite the remarkable success achieved by deep learning algorithms in various domains, such as computer vision, they remain vulnerable to adversarial perturbations. Adversarial Tr…
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Arshia Soltani Moakhar, Mohammad Azizmalayeri, Hossein Mirzaei +2
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentra…
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data
Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Ciná
Despite their success, Machine Learning (ML) models do not generalize effectively to data not originating from the training distribution. To reliably employ ML models in real-world…
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
Mohammad Azizmalayeri, Arman Zarei, Alireza Isavand +2
Current machine learning models achieve super-human performance in many real-world applications. Still, they are susceptible against imperceptible adversarial perturbations. The mo…
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations
Simin Shekarpaz, Mohammad Azizmalayeri, Mohammad Hossein Rohban
In this paper, we propose the physics informed adversarial training (PIAT) of neural networks for solving nonlinear differential equations (NDE). It is well-known that the standard…