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.IT2025
Algorithmic complexity of -expansions and application to A/D conversion
Valentin Abadie, Helmut Boelcskei
We establish diverse relationships between the algorithmic (Kolmogorov) complexity of the prefixes of any binary expansion and -expansions. These relationships allow to develop…