Abstract This paper introduces Non-Autonomous Input-Output Stable Network (NAIS-Net), a
very deep architecture where each stacked processing block is derived from a time-invariant …

Over the past decade, Deep Neural Networks (DNNs) have become very popular models for
problems involving massive amounts of data. The most successful DNNs tend to be …

Deep networks are commonly used to model dynamical systems, predicting how the state of
a system will evolve over time (either autonomously or in response to control inputs) …

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …

Low-precision deep neural network (DNN) training has gained tremendous attention as
reducing precision is one of the most effective knobs for boosting DNNs' training time/energy …

Modern deep neural networks increasingly make use of features such as control flow,
dynamic data structures, and dynamic tensor shapes. Existing deep learning systems focus …

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical
time-stepping algorithms to approximate solutions. Further, many systems characterized by …

Training a neural network with the gradient descent algorithm gives rise to a discrete-time
nonlinear dynamical system. Consequently, behaviors that are typically observed in these …

A main theoretical interest in biology and physics is to identify the nonlinear dynamical
system (DS) that generated observed time series. Recurrent Neural Networks (RNNs) are, in …

Recurrent neural networks (RNNs) are powerful models for processing time-series data, but
it remains challenging to understand how they function. Improving this understanding is of …