Sep 12, 2023 · We investigate the expressive power of deep residual neural networks idealized as continuous dynamical systems through control theory.
Sep 12, 2023 · We investigate the expressive power of deep residual neural networks idealized as continuous dynamical systems through control theory.
Under the assumption of affine invariance of the control family, a characterisation of universal interpolation is given, showing that it holds for ...
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Interpolation, Approximation and Controllability of Deep Neural Networks ... We investigate the expressive power of deep residual neural networks idealized as ...
We'll look at universal interpolation using the model of deep feedforward ... holds at every point x ∈ Rm, then controllability holds: any input can be ...
Abstract—In this article, we explore the effects of memory terms in continuous-layer Deep Residual Networks by studying. Neural ODEs (NODEs).
We make use of this perspective to link expressiveness of deep networks to the notion of controllability of dynamical systems. Using this connection, we study ...
Analysis of deep feedforward neural networks from an optimal control theory point of view: deep neural networks as discretizations of certain controlled ODEs.
In this article, we explore the effects of memory terms in continuous-layer Deep Residual Networks by studying Neural ODEs (NODEs). We investigate two types ...
... In this work, we study the ability of EKI to efficiently train neural ordinary differential equations (neural ODEs) in system identification and control ...