45 citations · 45 across the 5 of their papers we have counts for
6 papers
Recurrent neural networks approximate continuous functions
Valentin Abadie, Clemens Hutter, Helmut Bölcskei
Classical approximation theorems ask for a new neural network whenever the target accuracy is improved. This paper studies the opposite possibility: can the network be chosen once…
A Quantifier-Reversal Approximation Paradigm for Recurrent Neural Networks
Clemens Hutter, Valentin Abadie, Helmut Bölcskei
Classical neural network approximation results take the form: for every function and every error tolerance , one constructs a neural network whose architecture and weight…
Metric-Entropy Limits on the Approximation of Nonlinear Dynamical Systems
Yang Pan, Clemens Hutter, Helmut Bölcskei
This paper is concerned with fundamental limits on the approximation of nonlinear dynamical systems. Specifically, we show that recurrent neural networks (RNNs) can approximate non…
Metric entropy of causal, discrete-time LTI systems
Clemens Hutter, Thomas Allard, Helmut Bölcskei
In [1] it is shown that recurrent neural networks (RNNs) can learn - in a metric entropy optimal manner - discrete time, linear time-invariant (LTI) systems. This is effected by co…
Metric Entropy Limits on Recurrent Neural Network Learning of Linear Dynamical Systems
Clemens Hutter, Recep Gül, Helmut Bölcskei
One of the most influential results in neural network theory is the universal approximation theorem [1, 2, 3] which states that continuous functions can be approximated to within a…
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors
Clemens Hutter, Moritz von Stosch, Mariano Nicolas Cruz Bournazou +1
To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit dat…