Michael Tschannen
Michael Tschannen
Bestätigte E-Mail-Adresse bei apple.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Born again neural networks
T Furlanello, ZC Lipton, M Tschannen, L Itti, A Anandkumar
International Conference on Machine Learning (ICML), 1602-1611, 2018
4602018
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
2992017
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
2632018
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
2602019
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
1762018
On mutual information maximization for representation learning
M Tschannen*, J Djolonga*, PK Rubenstein, S Gelly, M Lucic
International Conference on Learning Representations (ICLR), 2020
1652020
Convolutional recurrent neural networks for electrocardiogram classification
M Zihlmann, D Perekrestenko, M Tschannen
Computing in Cardiology Conference (CinC) 44, 2017
1432017
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
96*2019
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
712019
Deep generative models for distribution-preserving lossy compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems (NeurIPS), 2018
662018
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
622016
A large-scale study of representation learning with the visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
arXiv preprint arXiv:1910.04867, 2019
61*2019
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
612018
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
552020
Weakly-supervised disentanglement without compromises
F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen
International Conference on Machine Learning (ICML), 2020
512020
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
502017
High-Fidelity Generative Image Compression
F Mentzer, G Toderici, M Tschannen, E Agustsson
Advances in Neural Information Processing Systems (NeurIPS), 2020
472020
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
402013
Dimensionality-reduced subspace clustering
R Heckel, M Tschannen, H Bölcskei
Information and Inference: A Journal of the IMA 6 (3), 246-283, 2017
362017
Self-supervised learning of video-induced visual invariances
M Tschannen, J Djolonga, M Ritter, A Mahendran, N Houlsby, S Gelly, ...
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
322020
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