4 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…
Wavelet-Based Density Estimation for Persistent Homology
Konstantin Häberle, Barbara Bravi, Anthea Monod
Persistent homology is a central methodology in topological data analysis that has been successfully implemented in many fields and is becoming increasingly popular and relevant. T…