no code implementations • 10 May 2024 • Tony Tohme, Mohammad Javad Khojasteh, Mohsen Sadr, Florian Meyer, Kamal Youcef-Toumi
The proposed ISR method naturally combines the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), a neural network-based symbolic architecture for function learning.
no code implementations • 30 Jan 2024 • Mohsen Sadr, Tony Tohme, Kamal Youcef-Toumi
Using variational calculus, we obtain an evolution equation for the Lagrange multipliers (adjoint equations) allowing us to compute the gradient of the objective function with respect to the parameters of PDEs given data in a straightforward manner.
no code implementations • 5 Oct 2023 • Aadi Kothari, Tony Tohme, Xiaotong Zhang, Kamal Youcef-Toumi
We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon.
no code implementations • 7 Jun 2023 • Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi, Nicolas G. Hadjiconstantinou
We validate the proposed MESSY estimation method against other benchmark methods for the case of a bi-modal and a discontinuous density, as well as a density at the limit of physical realizability.
no code implementations • 31 May 2022 • Tony Tohme, Dehong Liu, Kamal Youcef-Toumi
Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR).
no code implementations • 16 Sep 2021 • Tony Tohme, Kevin Vanslette, Kamal Youcef-Toumi
While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task.