| Born again neural networks T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar International Conference on Machine Learning (ICML), 1602-1611, 2018 | 212 | 2018 |
| Soft-to-hard vector quantization for end-to-end learning compressible representations E Agustsson, F Mentzer, M Tschannen, L Cavigelli, R Timofte, L Benini, ... Advances in Neural Information Processing Systems (NIPS), 1141-1151, 2017 | 199* | 2017 |
| Conditional probability models for deep image compression F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 148 | 2018 |
| Generative adversarial networks for extreme learned image compression E Agustsson*, M Tschannen*, F Mentzer*, R Timofte, L Van Gool International Conference on Computer Vision (ICCV), 2019 | 145 | 2019 |
| Convolutional recurrent neural networks for electrocardiogram classification M Zihlmann, D Perekrestenko, M Tschannen Computing in Cardiology Conference (CinC) 44, 2017 | 88 | 2017 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 85 | 2018 |
| High-fidelity image generation with fewer labels M Lucic*, M Tschannen*, M Ritter*, X Zhai, O Bachem, S Gelly International Conference on Machine Learning (ICML), 2019 | 50 | 2019 |
| On mutual information maximization for representation learning M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic International Conference on Learning Representations (ICLR), 2020 | 42 | 2020 |
| Heart sound classification using deep structured features M Tschannen, T Kramer, G Marti, M Heinzmann, T Wiatowski Computing in Cardiology Conference (CinC), 565-568, 2016 | 40 | 2016 |
| A learning-based approach for fast and robust vessel tracking in long ultrasound sequences V De Luca, M Tschannen, G Székely, C Tanner International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 39 | 2013 |
| A unified optimization view on generalized matching pursuit and Frank-Wolfe F Locatello, R Khanna*, M Tschannen*, M Jaggi Conference on Artificial Intelligence and Statistics (AISTATS), 860-868, 2017 | 37 | 2017 |
| Towards image understanding from deep compression without decoding R Torfason, F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool International Conference on Learning Representations (ICLR), 2018 | 33 | 2018 |
| Dimensionality-reduced subspace clustering R Heckel, M Tschannen, H Bölcskei Information and Inference: A Journal of the IMA 6 (3), 246-283, 2017 | 32 | 2017 |
| Deep generative models for distribution-preserving lossy compression M Tschannen, E Agustsson, M Lucic Advances in Neural Information Processing Systems (NeurIPS), 2018 | 29 | 2018 |
| Practical full resolution learned lossless image compression F Mentzer, E Agustsson, M Tschannen, R Timofte, L Van Gool IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 26 | 2019 |
| Subspace clustering of dimensionality-reduced data R Heckel, M Tschannen, H Bolcskei IEEE International Symposium on Information Theory (ISIT), 2997-3001, 2014 | 23 | 2014 |
| Discrete deep feature extraction: A theory and new architectures T Wiatowski, M Tschannen, A Stanic, P Grohs, H Bölcskei International Conference on Machine Learning (ICML), 2149-2158, 2016 | 22 | 2016 |
| Noisy subspace clustering via matching pursuits M Tschannen, H Bölcskei IEEE Transactions on Information Theory 64 (6), 4081-4104, 2018 | 21 | 2018 |
| Disentangling factors of variation using few labels F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem International Conference on Learning Representations (ICLR), 2020 | 18 | 2020 |
| Greedy algorithms for cone constrained optimization with convergence guarantees F Locatello, M Tschannen, G Rätsch, M Jaggi Advances in Neural Information Processing Systems (NIPS), 773-784, 2017 | 17 | 2017 |
Fabian MentzerPhD student, Computer Vision Laboratory, ETH ZurichVerified email at vision.ee.ethz.ch
