Reliable neural networks for regression uncertainty estimation
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 …
world problems, estimating their predictive uncertainty remains a challenging task. To …
GSR: A generalized symbolic regression approach
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 …
challenging problem in machine learning, and is known as Symbolic Regression (SR). In …
MESSY Estimation: Maximum-entropy based stochastic and symbolic density estimation
We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic
densitY estimation method. The proposed approach recovers probability density functions …
densitY estimation method. The proposed approach recovers probability density functions …
ISR: Invertible Symbolic Regression
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 …
technique that generates analytical relationships between inputs and outputs of a given …
Advances in Symbolic Regression: From Generalized Formulation to Density Estimation and Inverse Problem
T Tohme - 2024 - dspace.mit.edu
In this thesis, we explore the field of Symbolic Regression (SR), a middle ground between
simple linear regression and complex inscrutable black box regressors such as neural …
simple linear regression and complex inscrutable black box regressors such as neural …