22 citations · 64 across the 9 of their papers we have counts for
9 papers
VINA: Variational Invertible Neural Architectures
Shubhanshu Shekhar, Mohammad Javad Khojasteh, Ananya Acharya +2
The distinctive architectural features of normalizing flows (NFs), notably bijectivity and tractable Jacobians, make them well-suited for generative modeling. Invertible neural net…
ISR: Invertible Symbolic Regression
Tony Tohme, Mohammad Javad Khojasteh, Mohsen Sadr +2
We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning technique that generates analytical relationships between inputs and outputs of a given datase…
Data-Driven Discovery of PDEs via the Adjoint Method
Mohsen Sadr, Tony Tohme, Kamal Youcef-Toumi
In this work, we present an adjoint-based method for discovering the underlying governing partial differential equations (PDEs) given data. The idea is to consider a parameterized…
Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
Aadi Kothari, Tony Tohme, Xiaotong Zhang +1
Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of n…
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi +1
We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic densitY estimation method. The proposed approach recovers probability density functions symbolically…
GSR: A Generalized Symbolic Regression Approach
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). In contras…
Reliable Neural Networks for Regression Uncertainty Estimation
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. To addre…
A Generalized Bayesian Approach to Model Calibration
Tony Tohme, Kevin Vanslette, Kamal Youcef-Toumi
In model development, model calibration and validation play complementary roles toward learning reliable models. In this article, we expand the Bayesian Validation Metric framework…
A General Model Validation and Testing Tool
Kevin Vanslette, Tony Tohme, Kamal Youcef-Toumi
We construct and propose the "Bayesian Validation Metric" (BVM) as a general model validation and testing tool. We find the BVM to be capable of representing all of the standard va…