99 citations · 702 across the 46 of their papers we have counts for
7 papers · 1 filter
Monte Carlo-Type Neural Operator for Differential Equations
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a framework for learning solution operators of one-dimensional partial differential equations (PDEs) by directly learning the…
Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a lightweight architecture for learning solution operators for parametric PDEs by directly approximating the kernel integral…
Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
Salah Eddine Choutri, Boualem Djehiche, Prajwal Chauhan +1
We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional…
Koopman Autoencoders Learn Neural Representation Dynamics
Nishant Suresh Aswani, Saif Eddin Jabari
This paper explores a simple question: can we model the internal transformations of a neural network using dynamical systems theory? We introduce Koopman autoencoders to capture ho…
Neural operators struggle to learn complex PDEs in pedestrian mobility: Hughes model case study
Prajwal Chauhan, Salah Eddine Choutri, Mohamed Ghattassi +2
This paper investigates the limitations of neural operators in learning solutions for a Hughes model, a first-order hyperbolic conservation law system for crowd dynamics. The model…
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.…
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…