5 papers
Defending Against Frequency-Based Attacks with Diffusion Models
Fatemeh Amerehi, Patrick Healy
Adversarial training is a common strategy for enhancing model robustness against adversarial attacks. However, it is typically tailored to the specific attack types it is trained o…
Transforming Ridesharing: Harnessing Role Flexibility and HOV Integration for Enhanced Mobility Solutions
Fatemeh Amerehi, Patrick Healy
While dynamic ridesharing has been extensively studied, there remains a significant research gap in exploring role flexibility within the many-to-many ridesharing scheme, where the…
Narrowing Class-Wise Robustness Gaps in Adversarial Training
Fatemeh Amerehi, Patrick Healy
Efforts to address declining accuracy as a result of data shifts often involve various data-augmentation strategies. Adversarial training is one such method, designed to improve ro…
Label Augmentation for Neural Networks Robustness
Fatemeh Amerehi, Patrick Healy
Out-of-distribution generalization can be categorized into two types: common perturbations arising from natural variations in the real world and adversarial perturbations that are…
Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation
Yumnah Hasan, Allan de Lima, Fatemeh Amerehi +3
Medical imaging diagnosis increasingly relies on Machine Learning (ML) models. This is a task that is often hampered by severely imbalanced datasets, where positive cases can be qu…