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 …

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

Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Deep neural networks (DNNs) are well-known to be vulnerable to adversarial attacks, where
malicious human-imperceptible perturbations are included in the input to the deep network …

We introduce a new class of time-continuous recurrent neural network models. Instead of
declaring a learning system's dynamics by implicit nonlinearities, we construct networks of …

Continuous deep learning architectures have recently re-emerged as Neural Ordinary
Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the …

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those
that affect both node features and graph topology. This paper investigates GNNs derived …

A proper parametrization of state transition matrices of linear state-space models (SSMs)
followed by standard nonlinearities enables them to efficiently learn representations from …

Neural diffusion on graphs is a novel class of graph neural networks that has attracted
increasing attention recently. The capability of graph neural partial differential equations …