… Neural ODEs represent an important exception: they parameterize the time derivative of a
hidden state with a neural … making a piecewise-constant approximation to Neural ODEs to …

… -depth neural network, called the Neural Piecewise-Constant … Here, unlike the recently
proposed framework of the Neural … is the Neural Ordinary Differential Equations (NODEs), also …

… exist piecewise constant training weights for a neural ODE such that the solution of the continuity
equation corresponding to the neural ODE … equation of the neural ODE is approximately …

… exist piecewise constant training weights for a neural ODE such that the solution of the continuity
equation corresponding to the neural ODE … equation of the neural ODE is approximately …

… We provide numerical approximations to the infinite–dimensional problem, leading to novel
model variants, such as Galërkin and piecewise–constant Neural ODEs. Augmentation is …

… neural networks systems as well as impulsive neural networks systems with piecewise
constant … not an ability to adapt their structure and to learn in a way that a human being does. …

… We assume that the controls are piecewise constant. Consequently, on each of the time
subintervals [tk,tk+1) where they remain constant, a(t) ∈ Rd and b(t) ∈ R determine a unique …

… the neural networks to associative memory models, it is highly desired to ask whether the
neural … that the information been sent back and forth does not change after a few iterances, and …

… neural ODEs. When looking at contractivity properties of a one layer weight-tied neural ODE
… a one-layer neural ODE applied to the classification of the MNIST handwritten digits dataset. …

… architecture does not “see” the parameter P and in practice memory footprint does not scale
… We generate training data by setting ν to different piecewise constant functions of three …