123 citations · 398 across the 47 of their papers we have counts for
68 papers
Separation Capacity of Scattering Networks on Low-Dimensional Datasets
Konstantin Häberle, Helmut Bölcskei
We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consider employ a fixed monomial non…
Function-Counting Theory for Low-Dimensional Data Structures
Konstantin Häberle, Helmut Bölcskei
The success of deep learning models in classification and regression is widely attributed to the low-dimensional structure that real-world data tend to exhibit, despite their high-…
Separation Capacity of Scattering Networks
Konstantin Häberle, Helmut Bölcskei
In this paper, we attempt to enhance the theoretical understanding of convolutional neural networks (CNNs) as feature extractors in classification tasks by analyzing them through t…
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…
Complete Identification of Deep ReLU Networks through Łukasiewicz Logic
Yani Zhang, Helmut Bölcskei
Two deep ReLU networks can have entirely different architectures and parameters, yet realize the same function. We provide a complete characterization of this nonuniqueness. This i…
Generating Rectifiable Measures through Neural Networks
Erwin Riegler, Alex Bühler, Yang Pan +1
We derive universal approximation results for the class of (countably) -rectifiable measures. Specifically, we prove that -rectifiable measures can be approximated as push-fo…