22 citations · 64 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…