31 citations · 186 across the 67 of their papers we have counts for
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InForecaster: Forecasting Influenza Hemagglutinin Mutations Through the Lens of Anomaly Detection
Ali Garjani, Atoosa Malemir Chegini, Mohammadreza Salehi +6
The influenza virus hemagglutinin is an important part of the virus attachment to the host cells. The hemagglutinin proteins are one of the genetic regions of the virus with a high…
Your Out-of-Distribution Detection Method is Not Robust!
Mohammad Azizmalayeri, Arshia Soltani Moakhar, Arman Zarei +3
Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD det…
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…
SwinCheX: Multi-label classification on chest X-ray images with transformers
Sina Taslimi, Soroush Taslimi, Nima Fathi +2
According to the considerable growth in the avail of chest X-ray images in diagnosing various diseases, as well as gathering extensive datasets, having an automated diagnosis proce…
OOD Augmentation May Be at Odds with Open-Set Recognition
Mohammad Azizmalayeri, Mohammad Hossein Rohban
Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in…
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection
Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi +4
We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face a dramatic drop under the so-called "near-distribution" setting, where…