A mathematical theory of deep convolutional neural networks for feature extraction T Wiatowski, H Bölcskei IEEE Transactions on Information Theory 64 (3), 1845-1866, 2017 | 463 | 2017 |

Heart sound classification using deep structured features M Tschannen, T Kramer, G Marti, M Heinzmann, T Wiatowski 2016 computing in Cardiology conference (CinC), 565-568, 2016 | 116 | 2016 |

Deep convolutional neural networks based on semi-discrete frames T Wiatowski, H Bölcskei 2015 IEEE International Symposium on Information Theory (ISIT), 1212-1216, 2015 | 39 | 2015 |

Energy propagation in deep convolutional neural networks T Wiatowski, P Grohs, H Bölcskei IEEE Transactions on Information Theory 64 (7), 4819-4842, 2017 | 32 | 2017 |

Deep convolutional neural networks on cartoon functions P Grohs, T Wiatowski, H Bölcskei 2016 IEEE International Symposium on Information Theory (ISIT), 1163-1167, 2016 | 32 | 2016 |

Discrete deep feature extraction: A theory and new architectures T Wiatowski, M Tschannen, A Stanic, P Grohs, H Bölcskei International Conference on Machine Learning, 2149-2158, 2016 | 28 | 2016 |

Deep structured features for semantic segmentation M Tschannen, L Cavigelli, F Mentzer, T Wiatowski, L Benini 2017 25th European Signal Processing Conference (EUSIPCO), 61-65, 2017 | 16 | 2017 |

Topology reduction in deep convolutional feature extraction networks T Wiatowski, P Grohs, H Bölcskei Wavelets and Sparsity XVII 10394, 269-280, 2017 | 6 | 2017 |

Energy decay and conservation in deep convolutional neural networks P Grohs, T Wiatowski, H Bcolcskei 2017 IEEE International Symposium on Information Theory (ISIT), 1356-1360, 2017 | 4 | 2017 |

Harmonic Analysis of Deep Convolutional Neural Networks T Wiatowski ETH Zurich, 2018 | | 2018 |

Supplement: Discrete Deep Feature Extraction: A Theory and New Architectures T Wiatowski, EE ETHZ, M Tschannen, A Stanic, E CH, P Grohs, ... | | |