no code implementations • 10 Jul 2017 • Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
Finally, for networks based on Weyl-Heisenberg filters, we determine the prototype function bandwidth that minimizes---for fixed network depth $N$---the average number of operationally significant nodes per layer.
no code implementations • 12 Apr 2017 • Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
This paper establishes conditions for energy conservation (and thus for a trivial null-set) for a wide class of deep convolutional neural network-based feature extractors and characterizes corresponding feature map energy decay rates.
no code implementations • 26 Sep 2016 • Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini
We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms.
no code implementations • 26 May 2016 • Thomas Wiatowski, Michael Tschannen, Aleksandar Stanić, Philipp Grohs, Helmut Bölcskei
First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and B\"olcskei, 2015.
no code implementations • 29 Apr 2016 • Philipp Grohs, Thomas Wiatowski, Helmut Bölcskei
Wiatowski and B\"olcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities.
no code implementations • 19 Dec 2015 • Thomas Wiatowski, Helmut Bölcskei
Deep convolutional neural networks have led to breakthrough results in numerous practical machine learning tasks such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go, and image captioning.
no code implementations • 21 Apr 2015 • Thomas Wiatowski, Helmut Bölcskei
Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat.