Michael Tschannen
Michael Tschannen
Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
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
161*2017
Born again neural networks
T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar
International Conference on Machine Learning (ICML), 1602-1611, 2018
1412018
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
1112019
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
1052018
Convolutional recurrent neural networks for electrocardiogram classification
M Zihlmann, D Perekrestenko, M Tschannen
Computing in Cardiology Conference (CinC) 44, 2017
712017
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
582018
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
362013
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
342017
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
312016
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
292019
Dimensionality-reduced subspace clustering
R Heckel, M Tschannen, H Bölcskei
Information and Inference: A Journal of the IMA 6 (3), 246-283, 2017
292017
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
272018
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
222016
Subspace clustering of dimensionality-reduced data
R Heckel, M Tschannen, H Bolcskei
IEEE International Symposium on Information Theory (ISIT), 2997-3001, 2014
222014
Deep generative models for distribution-preserving lossy compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems (NeurIPS), 2018
172018
Noisy subspace clustering via matching pursuits
M Tschannen, H Bölcskei
IEEE Transactions on Information Theory 64 (6), 4081-4104, 2018
172018
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
162017
StrassenNets: Deep learning with a multiplication budget
M Tschannen, A Khanna, A Anandkumar
International Conference on Machine Learning (ICML), 4992-5001, 2018
142018
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
122019
On mutual information maximization for representation learning
M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations (ICLR), 2020
102020
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Articles 1–20