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 …

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 …

Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …

Graph neural networks (GNN) are powerful tools in many web research problems. However,
existing GNNs are not fully suitable for many real-world web applications. For example, over …

ABSTRACT We propose GRAph Neural Diffusion with a source term (GRAND++) for graph
deep learning with a limited number of labeled nodes, ie, low-labeling rate. GRAND++ is a …

Neural ordinary differential equations describe how values change in time. This is the
reason why they gained importance in modeling sequential data, especially when the …

We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the
continuous limit of the classical momentum accelerated gradient descent, to improve neural …

Abstract Group equivariance (eg SE (3) equivariance) is a critical physical symmetry in
science, from classical and quantum physics to computational biology. It enables robust and …

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