Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Neural ordinary differential equations are an attractive option for modelling temporal
dynamics. However, a fundamental issue is that the solution to an ordinary differential …

Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal
dynamics. However, a fundamental limitation has been that such models have typically been …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

The sampling of probability distributions specified up to a normalization constant is an
important problem in both machine learning and statistical mechanics. While classical …

Generative adversarial networks (GANs) have been extremely successful in generating
samples, from seemingly high dimensional probability measures. However, these methods …

Deep learning has enjoyed much recent success, and applying state-of-the-art model
learning methods to controls is an exciting prospect. However, there is a strong reluctance to …

Abstract Training Neural Ordinary Differential Equations (ODEs) is often computationally
expensive. Indeed, computing the forward pass of such models involves solving an ODE …