3 papers
cs.LG2026
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…
cs.NE2025
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 weigh…
cs.LG2025
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…