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2020 – today
- 2024
[j3]Aleksandar Stanic, Sergi Caelles, Michael Tschannen:
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. Trans. Mach. Learn. Res. 2024 (2024)- [c39]Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut:
On Scaling Up a Multilingual Vision and Language Model. CVPR 2024: 14432-14444 - [c38]Michael Tschannen, Cian Eastwood, Fabian Mentzer:
GIVT: Generative Infinite-Vocabulary Transformers. ECCV (57) 2024: 292-309 - [c37]Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen:
Finite Scalar Quantization: VQ-VAE Made Simple. ICLR 2024 - [i44]Aleksandar Stanic, Sergi Caelles, Michael Tschannen:
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. CoRR abs/2401.01974 (2024) - [i43]Bo Wan, Michael Tschannen, Yongqin Xian, Filip Pavetic, Ibrahim Alabdulmohsin, Xiao Wang, André Susano Pinto, Andreas Steiner, Lucas Beyer, Xiaohua Zhai:
LocCa: Visual Pretraining with Location-aware Captioners. CoRR abs/2403.19596 (2024) - [i42]Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner
, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey A. Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bosnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier J. Hénaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai:
PaliGemma: A versatile 3B VLM for transfer. CoRR abs/2407.07726 (2024) - 2023
- [c36]Michael Tschannen, Basil Mustafa, Neil Houlsby:
CLIPPO: Image-and-Language Understanding from Pixels Only. CVPR 2023: 11006-11017 - [c35]Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic:
FlexiViT: One Model for All Patch Sizes. CVPR 2023: 14496-14506 - [c34]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen:
M2T: Masking Transformers Twice for Faster Decoding. ICCV 2023: 5317-5326 - [c33]Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby:
Scaling Vision Transformers to 22 Billion Parameters. ICML 2023: 7480-7512 - [c32]Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer:
Image Captioners Are Scalable Vision Learners Too. NeurIPS 2023 - [i41]Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby:
Scaling Vision Transformers to 22 Billion Parameters. CoRR abs/2302.05442 (2023) - [i40]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen:
M2T: Masking Transformers Twice for Faster Decoding. CoRR abs/2304.07313 (2023) - [i39]Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut:
PaLI-X: On Scaling up a Multilingual Vision and Language Model. CoRR abs/2305.18565 (2023) - [i38]Michael Tschannen, Manoj Kumar, Andreas Steiner, Xiaohua Zhai, Neil Houlsby, Lucas Beyer:
Image Captioners Are Scalable Vision Learners Too. CoRR abs/2306.07915 (2023) - [i37]Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen:
Finite Scalar Quantization: VQ-VAE Made Simple. CoRR abs/2309.15505 (2023) - [i36]Michael Tschannen, Cian Eastwood, Fabian Mentzer:
GIVT: Generative Infinite-Vocabulary Transformers. CoRR abs/2312.02116 (2023) - 2022
- [c31]Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, Michael Tschannen:
Neural Face Video Compression using Multiple Views. CVPR Workshops 2022: 1737-1741 - [i35]Anna Volokitin, Stefan Brugger, Ali Benlalah, Sebastian Martin, Brian Amberg, Michael Tschannen:
Neural Face Video Compression using Multiple Views. CoRR abs/2203.15401 (2022) - [i34]Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic:
FlexiViT: One Model for All Patch Sizes. CoRR abs/2212.08013 (2022) - [i33]Michael Tschannen, Basil Mustafa, Neil Houlsby:
Image-and-Language Understanding from Pixels Only. CoRR abs/2212.08045 (2022) - 2021
- [c30]Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic:
On Robustness and Transferability of Convolutional Neural Networks. CVPR 2021: 16458-16468 - [c29]Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic:
Representation learning from videos in-the-wild: An object-centric approach. WACV 2021: 177-187 - 2020
- [c28]Fabian Mentzer, Luc Van Gool, Michael Tschannen:
Learning Better Lossless Compression Using Lossy Compression. CVPR 2020: 6637-6646 - [c27]Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic:
Self-Supervised Learning of Video-Induced Visual Invariances. CVPR 2020: 13803-13812 - [c26]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variations Using Few Labels. ICLR 2020 - [c25]Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic:
On Mutual Information Maximization for Representation Learning. ICLR 2020 - [c24]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. ICML 2020: 6348-6359 - [c23]Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen:
Automatic Shortcut Removal for Self-Supervised Representation Learning. ICML 2020: 6927-6937 - [c22]Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson:
High-Fidelity Generative Image Compression. NeurIPS 2020 - [i32]Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen:
Weakly-Supervised Disentanglement Without Compromises. CoRR abs/2002.02886 (2020) - [i31]Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen:
Automatic Shortcut Removal for Self-Supervised Representation Learning. CoRR abs/2002.08822 (2020) - [i30]Fabian Mentzer, Luc Van Gool, Michael Tschannen:
Learning Better Lossless Compression Using Lossy Compression. CoRR abs/2003.10184 (2020) - [i29]Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson:
High-Fidelity Generative Image Compression. CoRR abs/2006.09965 (2020) - [i28]Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic:
On Robustness and Transferability of Convolutional Neural Networks. CoRR abs/2007.08558 (2020) - [i27]Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic:
Representation learning from videos in-the-wild: An object-centric approach. CoRR abs/2010.02808 (2020)
2010 – 2019
- 2019
- [b1]Michael Tschannen:
Unsupervised learning: model-based clustering and learned compression. ETH Zurich, Zürich, Switzerland, Hartung-Gorre Verlag 2019, ISBN 978-3-86628-637-5, pp. 1-205 - [c21]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Practical Full Resolution Learned Lossless Image Compression. CVPR 2019: 10629-10638 - [c20]Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool:
Generative Adversarial Networks for Extreme Learned Image Compression. ICCV 2019: 221-231 - [c19]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. ICML 2019: 4183-4192 - [i26]Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. CoRR abs/1903.02271 (2019) - [i25]Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem:
Disentangling Factors of Variation Using Few Labels. CoRR abs/1905.01258 (2019) - [i24]Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic:
On Mutual Information Maximization for Representation Learning. CoRR abs/1907.13625 (2019) - [i23]Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby:
The Visual Task Adaptation Benchmark. CoRR abs/1910.04867 (2019) - [i22]Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic:
Semantic Bottleneck Scene Generation. CoRR abs/1911.11357 (2019) - [i21]Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic:
Self-Supervised Learning of Video-Induced Visual Invariances. CoRR abs/1912.02783 (2019) - 2018
- [j2]Michael Tschannen, Helmut Bölcskei:
Noisy Subspace Clustering via Matching Pursuits. IEEE Trans. Inf. Theory 64(6): 4081-4104 (2018) - [c18]Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool:
Extreme Learned Image Compression with GANs. CVPR Workshops 2018: 2587-2590 - [c17]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Conditional Probability Models for Deep Image Compression. CVPR 2018: 4394-4402 - [c16]Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Towards Image Understanding from Deep Compression Without Decoding. ICLR (Poster) 2018 - [c15]Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born-Again Neural Networks. ICML 2018: 1602-1611 - [c14]Michael Tschannen, Aran Khanna, Animashree Anandkumar:
StrassenNets: Deep Learning with a Multiplication Budget. ICML 2018: 4992-5001 - [c13]Michael Tschannen, Eirikur Agustsson, Mario Lucic:
Deep Generative Models for Distribution-Preserving Lossy Compression. NeurIPS 2018: 5933-5944 - [i20]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Conditional Probability Models for Deep Image Compression. CoRR abs/1801.04260 (2018) - [i19]Robert Torfason, Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Towards Image Understanding from Deep Compression without Decoding. CoRR abs/1803.06131 (2018) - [i18]Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc Van Gool:
Generative Adversarial Networks for Extreme Learned Image Compression. CoRR abs/1804.02958 (2018) - [i17]Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born Again Neural Networks. CoRR abs/1805.04770 (2018) - [i16]Michael Tschannen, Eirikur Agustsson, Mario Lucic:
Deep Generative Models for Distribution-Preserving Lossy Compression. CoRR abs/1805.11057 (2018) - [i15]Fabian Mentzer, Eirikur Agustsson, Michael Tschannen, Radu Timofte, Luc Van Gool:
Practical Full Resolution Learned Lossless Image Compression. CoRR abs/1811.12817 (2018) - [i14]Michael Tschannen, Olivier Bachem, Mario Lucic:
Recent Advances in Autoencoder-Based Representation Learning. CoRR abs/1812.05069 (2018) - 2017
- [c12]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. AISTATS 2017: 860-868 - [c11]Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen:
Convolutional Recurrent Neural Networks for Electrocardiogram Classification. CinC 2017 - [c10]Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini:
Deep structured features for semantic segmentation. EUSIPCO 2017: 61-65 - [c9]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. NIPS 2017: 773-784 - [c8]Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool:
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. NIPS 2017: 1141-1151 - [i13]Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi:
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. CoRR abs/1702.06457 (2017) - [i12]Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc Van Gool:
Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks. CoRR abs/1704.00648 (2017) - [i11]Francesco Locatello, Michael Tschannen, Gunnar Rätsch, Martin Jaggi:
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. CoRR abs/1705.11041 (2017) - [i10]Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen:
Convolutional Recurrent Neural Networks for Electrocardiogram Classification. CoRR abs/1710.06122 (2017) - [i9]Michael Tschannen, Aran Khanna, Anima Anandkumar:
StrassenNets: Deep learning with a multiplication budget. CoRR abs/1712.03942 (2017) - 2016
- [j1]Michael Tschannen, Lazaros Vlachopoulos, Christian Gerber, Gábor Székely, Philipp Fürnstahl:
Regression forest-based automatic estimation of the articular margin plane for shoulder prosthesis planning. Medical Image Anal. 31: 88-97 (2016) - [c7]Michael Tschannen, Thomas Kramer, Gian Marti, Matthias Heinzmann, Thomas Wiatowski:
Heart Sound Classification Using Deep Structured Features. CinC 2016 - [c6]Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei:
Discrete Deep Feature Extraction: A Theory and New Architectures. ICML 2016: 2149-2158 - [i8]Rajiv Khanna, Michael Tschannen, Martin Jaggi:
Pursuits in Structured Non-Convex Matrix Factorizations. CoRR abs/1602.04208 (2016) - [i7]Thomas Wiatowski, Michael Tschannen, Aleksandar Stanic, Philipp Grohs, Helmut Bölcskei:
Discrete Deep Feature Extraction: A Theory and New Architectures. CoRR abs/1605.08283 (2016) - [i6]Michael Tschannen, Lukas Cavigelli, Fabian Mentzer, Thomas Wiatowski, Luca Benini:
Deep Structured Features for Semantic Segmentation. CoRR abs/1609.07916 (2016) - [i5]Michael Tschannen, Helmut Bölcskei:
Robust nonparametric nearest neighbor random process clustering. CoRR abs/1612.01103 (2016) - [i4]Michael Tschannen, Helmut Bölcskei:
Noisy subspace clustering via matching pursuits. CoRR abs/1612.03450 (2016) - 2015
- [c5]Michael Tschannen, Helmut Bölcskei:
Nonparametric nearest neighbor random process clustering. ISIT 2015: 1207-1211 - [i3]Michael Tschannen, Helmut Bölcskei:
Nonparametric Nearest Neighbor Random Process Clustering. CoRR abs/1504.05059 (2015) - [i2]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Dimensionality-reduced subspace clustering. CoRR abs/1507.07105 (2015) - 2014
- [c4]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Subspace clustering of dimensionality-reduced data. ISIT 2014: 2997-3001 - [i1]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Subspace clustering of dimensionality-reduced data. CoRR abs/1404.6818 (2014) - 2013
- [c3]Valeria De Luca, Michael Tschannen, Gábor Székely, Christine Tanner:
A Learning-Based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences. MICCAI (1) 2013: 518-525 - 2012
- [c2]Michael Tschannen, Grzegorz Toporek, Daphné Wallach, Matthias Peterhans, Stefan Weber:
Single Marker Localization for Automatic Patient Registration in Interventional Radiology. CURAC 2012: 31-34 - [c1]Marc Rennhard, Michael Tschannen, Tobias Christen:
SecureSafe: a highly secure online data safe industrial use case. MPM@EuroSys 2012: 1:1-1:6
Coauthor Index
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