Ruth E. Baker

A Likelihood-Based Framework for Simultaneously Learning Both Noise and Growth Dynamics Using Biologically-Informed Neural Networks

In recent years, neural ordinary differential equation frameworks such as Biologically-Informed Neural Networks (BINNs) have shown promise for learning mechanistic laws from sparse data. However, most existing approaches implicitly assume homoscedastic Gaussian noise, and therefo...