Abstract Neural Ordinary Differential Equations (NODEs) have proven successful in learning
dynamical systems in terms of accurately recovering the observed trajectories. While …

Circuits of biological neurons, such as in the functional parts of the brain can be modeled as
networks of coupled oscillators. Inspired by the ability of these systems to express a rich set …

Recent deep learning (DL) models are moving more and more to dynamic neural network
(NN) architectures, where the NN structure changes for every data sample. However …

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …

The design of neural network layers plays a crucial role in determining the efficiency and
performance of various computer vision tasks. However, most existing layers compromise …

Converging evidence suggests the brain encodes time in dynamic patterns of neural activity,
including neural sequences, ramping activity, and complex dynamics. Most temporal tasks …

Neurons in the brain use an event signal, termed spike, encode temporal information for
neural computation. Spiking neural networks (SNNs) take this advantage to serve as …

Modern embedded cyber-physical systems are becoming entangled with the realm of deep
neural networks (DNNs) towards increased autonomy. While applying DNNs can …

Despite their success in massive engineering applications, deep neural networks are
vulnerable to various perturbations due to their black-box nature. Recent study has shown …

Processing sequential data of variable length is a major challenge in a wide range of
applications, such as speech recognition, language modeling, generative image modeling …