3 papers
cs.LG2026
FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting
Fares B. Mehouachi, Saif Eddin Jabari
We introduce FlowMixer, a single-layer neural architecture that leverages constrained matrix operations to model structured spatiotemporal patterns with enhanced interpretability.…
cs.LG2026
Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless Norm Solution for Fast Adversarial Training
Fares B. Mehouachi, Saif Eddin Jabari
Adversarial training is a cornerstone of robust deep learning, but fast methods like the Fast Gradient Sign Method (FGSM) often suffer from Catastrophic Overfitting (CO), where mod…
eess.SY2024
Urban traffic analysis and forecasting through shared Koopman eigenmodes
Chuhan Yang, Fares B. Mehouachi, Monica Menendez +1
Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common…