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
In particular, for a family of parameterized and nonlinear PDEs, we show how the corresponding adjoint equations can be derived.
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.