10 citations · 12 across the 2 of their papers we have counts for
7 papers
Topology Reduction in Deep Convolutional Feature Extraction Networks
Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and ,s of nodes. Such network sizes entail significant computational compl…
Energy Propagation in Deep Convolutional Neural Networks
Thomas Wiatowski, Philipp Grohs, Helmut Bölcskei
Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the netwo…
Deep Structured Features for Semantic Segmentation
Michael Tschannen, Lukas Cavigelli, Fabian Mentzer +2
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. Speci…
Discrete Deep Feature Extraction: A Theory and New Architectures
Thomas Wiatowski, Michael Tschannen, Aleksandar Stanić +2
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…
Convergence of a Strang splitting finite element discretization for the Schrödinger-Poisson equation
Winfried Auzinger, Thomas Kassebacher, Othmar Koch +1
Operator splitting methods combined with finite element spatial discretizations are studied for time-dependent nonlinear Schrödinger equations. In particular, the Schrödinger-Poiss…
A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
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-…