3 papers
stat.ML2026
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…
stat.ML2026
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-…
stat.ML2026
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…