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 find that the addition of a symbolic search for basis functions improves the accuracy of the estimation at a reasonable additional computational cost.
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.
1 code implementation • 20 Aug 2021 • Alexandre Heuillet, Hedi Tabia, Hichem Arioui, Kamal Youcef-Toumi
This approach is accompanied by a novel metric that measures the distance between architectures inside our custom search space.
1 code implementation • 26 Oct 2020 • Quang H. Le, Kamal Youcef-Toumi, Dzmitry Tsetserukou, Ali Jahanian
Rethinking reconstruction networks as a generator, we define the problem of predicting masks as a GANs game framework: A segmentation network generates the masks, and a discriminator network decides on the quality of the masks.
no code implementations • 25 Sep 2019 • Mikio Furokawa, Erik Gest, Takayuki Hirano, Kamal Youcef-Toumi
However, it is difficult to perfectly control the collection timing of the measurements.