5 papers
Accuracy is Not Enough: Poisoning Interpretability in Federated Learning via Color Skew
Farhin Farhad Riya, Shahinul Hoque, Jinyuan Stella Sun +1
As machine learning models are increasingly deployed in safety-critical domains, visual explanation techniques have become essential tools for supporting transparency. In this work…
HEMERA: A Human-Explainable Transformer Model for Estimating Lung Cancer Risk using GWAS Data
Maria Mahbub, Robert J. Klein, Myvizhi Esai Selvan +12
Lung cancer (LC) is the third most common cancer and the leading cause of cancer deaths in the US. Although smoking is the primary risk factor, the occurrence of LC in never-smoker…
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
Sahil Tyagi, Andrei Cozma, Olivera Kotevska +1
Federated Learning (FL) is critical for edge and High Performance Computing (HPC) where data is not centralized and privacy is crucial. We present OmniFed, a modular framework desi…
Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations
Joel Brogan, Olivera Kotevska, Anibely Torres +2
Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in t…
Increasing city safety awareness regarding disruptive traffic stream
Olivera Kotevska
Transportation systems serve the people in essence, in this study we focus in traffic information related to violation events to respond to safety requirements of the cities. Traff…