Single Marker Localization for Automatic Patient Registration in Interventional Radiology. - Michael Tschannen; Grzegorz Toporek; Daphné Wallach; Matthias Peterhans; Stefan Weber 002 (CURAC, 2012)

SecureSafe: a highly secure online data safe industrial use case. - Marc Rennhard; Michael Tschannen; Tobias Christen (MPM@EuroSys, 2012)

A Learning-Based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences. - Valeria De Luca; Michael Tschannen; Gábor Székely; Christine Tanner (MICCAI (1), 2013)

Subspace clustering of dimensionality-reduced data. - Reinhard Heckel; Michael Tschannen; Helmut Bölcskei (ISIT, 2014)

Nonparametric nearest neighbor random process clustering. - Michael Tschannen; Helmut Bölcskei (ISIT, 2015)

Dimensionality-reduced subspace clustering. - Reinhard Heckel; Michael Tschannen; Helmut Bölcskei (CoRR, 2015)

Heart Sound Classification Using Deep Structured Features. - Michael Tschannen; Thomas Kramer; Gian Marti; Matthias Heinzmann; Thomas Wiatowski (CinC, 2016)

Discrete Deep Feature Extraction: A Theory and New Architectures. - Thomas Wiatowski; Michael Tschannen; Aleksandar Stanic; Philipp Grohs; Helmut Bölcskei (ICML, 2016)

Pursuits in Structured Non-Convex Matrix Factorizations. - Rajiv Khanna; Michael Tschannen; Martin Jaggi (CoRR, 2016)

Robust nonparametric nearest neighbor random process clustering. - Michael Tschannen; Helmut Bölcskei (CoRR, 2016)

Regression forest-based automatic estimation of the articular margin plane for shoulder prosthesis planning. - Michael Tschannen; Lazaros Vlachopoulos; Christian Gerber; Gábor Székely; Philipp Fürnstahl (Medical Image Anal., 2016)

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. - Francesco Locatello; Rajiv Khanna; Michael Tschannen; Martin Jaggi (AISTATS, 2017)

Convolutional Recurrent Neural Networks for Electrocardiogram Classification. - Martin Zihlmann; Dmytro Perekrestenko; Michael Tschannen (CinC, 2017)

Deep structured features for semantic segmentation. - Michael Tschannen; Lukas Cavigelli; Fabian Mentzer; Thomas Wiatowski; Luca Benini (EUSIPCO, 2017)

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. - Eirikur Agustsson; Fabian Mentzer; Michael Tschannen; Lukas Cavigelli; Radu Timofte; Luca Benini; Luc Van Gool (NIPS, 2017)

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. - Francesco Locatello; Michael Tschannen; Gunnar Rätsch; Martin Jaggi (NIPS, 2017)

Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks. - Eirikur Agustsson; Fabian Mentzer; Michael Tschannen; Lukas Cavigelli; Radu Timofte; Luca Benini; Luc Van Gool (CoRR, 2017)

Extreme Learned Image Compression with GANs. - Eirikur Agustsson; Michael Tschannen; Fabian Mentzer; Radu Timofte; Luc Van Gool (CVPR Workshops, 2018)

Conditional Probability Models for Deep Image Compression. - Fabian Mentzer; Eirikur Agustsson; Michael Tschannen; Radu Timofte; Luc Van Gool (CVPR, 2018)

Towards Image Understanding from Deep Compression Without Decoding. - Robert Torfason; Fabian Mentzer; Eirikur Agustsson; Michael Tschannen; Radu Timofte; Luc Van Gool (ICLR (Poster), 2018)

Born-Again Neural Networks. - Tommaso Furlanello; Zachary Chase Lipton; Michael Tschannen; Laurent Itti; Anima Anandkumar (ICML, 2018)

StrassenNets: Deep Learning with a Multiplication Budget. - Michael Tschannen; Aran Khanna; Animashree Anandkumar (ICML, 2018)

Deep Generative Models for Distribution-Preserving Lossy Compression. - Michael Tschannen; Eirikur Agustsson; Mario Lucic (NeurIPS, 2018)

Recent Advances in Autoencoder-Based Representation Learning. - Michael Tschannen; Olivier Bachem; Mario Lucic (CoRR, 2018)

Noisy Subspace Clustering via Matching Pursuits. - Michael Tschannen; Helmut Bölcskei (IEEE Trans. Inf. Theory, 2018)

Practical Full Resolution Learned Lossless Image Compression. - Fabian Mentzer; Eirikur Agustsson; Michael Tschannen; Radu Timofte; Luc Van Gool (CVPR, 2019)

Generative Adversarial Networks for Extreme Learned Image Compression. - Eirikur Agustsson; Michael Tschannen; Fabian Mentzer; Radu Timofte; Luc Van Gool (ICCV, 2019)

High-Fidelity Image Generation With Fewer Labels. - Mario Lucic; Michael Tschannen; Marvin Ritter; Xiaohua Zhai; Olivier Bachem; Sylvain Gelly (ICML, 2019)

Disentangling Factors of Variation Using Few Labels. - Francesco Locatello; Michael Tschannen; Stefan Bauer; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem (CoRR, 2019)

The Visual Task Adaptation Benchmark. - Xiaohua Zhai; Joan Puigcerver; Alexander Kolesnikov 003; Pierre Ruyssen; Carlos Riquelme; Mario Lucic; Josip Djolonga; André Susano Pinto; Maxim Neumann; Alexey Dosovitskiy; Lucas Beyer; Olivier Bachem; et al. (CoRR, 2019)

Semantic Bottleneck Scene Generation. - Samaneh Azadi; Michael Tschannen; Eric Tzeng; Sylvain Gelly; Trevor Darrell; Mario Lucic (CoRR, 2019)

Unsupervised learning: model-based clustering and learned compression. - Michael Tschannen (Hartung-Gorre Verlag, 2019)

Learning Better Lossless Compression Using Lossy Compression. - Fabian Mentzer; Luc Van Gool; Michael Tschannen (CVPR, 2020)

Self-Supervised Learning of Video-Induced Visual Invariances. - Michael Tschannen; Josip Djolonga; Marvin Ritter; Aravindh Mahendran; Neil Houlsby; Sylvain Gelly; Mario Lucic (CVPR, 2020)

Disentangling Factors of Variations Using Few Labels. - Francesco Locatello; Michael Tschannen; Stefan Bauer; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem (ICLR, 2020)

On Mutual Information Maximization for Representation Learning. - Michael Tschannen; Josip Djolonga; Paul K. Rubenstein; Sylvain Gelly; Mario Lucic (ICLR, 2020)

Weakly-Supervised Disentanglement Without Compromises. - Francesco Locatello; Ben Poole; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem; Michael Tschannen (ICML, 2020)

Automatic Shortcut Removal for Self-Supervised Representation Learning. - Matthias Minderer; Olivier Bachem; Neil Houlsby; Michael Tschannen (ICML, 2020)

High-Fidelity Generative Image Compression. - Fabian Mentzer; George Toderici; Michael Tschannen; Eirikur Agustsson (NeurIPS, 2020)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

Representation learning from videos in-the-wild: An object-centric approach. - Rob Romijnders; Aravindh Mahendran; Michael Tschannen; Josip Djolonga; Marvin Ritter; Neil Houlsby; Mario Lucic (WACV, 2021)

Neural Face Video Compression using Multiple Views. - Anna Volokitin; Stefan Brugger; Ali Benlalah; Sebastian Martin; Brian Amberg; Michael Tschannen (CVPR Workshops, 2022)

Image-and-Language Understanding from Pixels Only. - Michael Tschannen; Basil Mustafa; Neil Houlsby (CoRR, 2022)

FlexiViT: One Model for All Patch Sizes. - Lucas Beyer; Pavel Izmailov; Alexander Kolesnikov 003; Mathilde Caron; Simon Kornblith; Xiaohua Zhai; Matthias Minderer; Michael Tschannen; Ibrahim Alabdulmohsin; Filip Pavetic (CVPR, 2023)

CLIPPO: Image-and-Language Understanding from Pixels Only. - Michael Tschannen; Basil Mustafa; Neil Houlsby (CVPR, 2023)

M2T: Masking Transformers Twice for Faster Decoding. - Fabian Mentzer; Eirikur Agustsson; Michael Tschannen (ICCV, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Image Captioners Are Scalable Vision Learners Too. - Michael Tschannen; Manoj Kumar; Andreas Steiner 001; Xiaohua Zhai; Neil Houlsby; Lucas Beyer (NeurIPS, 2023)

PaLI-X: On Scaling up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CoRR, 2023)

On Scaling Up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CVPR, 2024)

GIVT: Generative Infinite-Vocabulary Transformers. - Michael Tschannen; Cian Eastwood; Fabian Mentzer (ECCV (57), 2024)

Finite Scalar Quantization: VQ-VAE Made Simple. - Fabian Mentzer; David Minnen; Eirikur Agustsson; Michael Tschannen (ICLR, 2024)

LocCa: Visual Pretraining with Location-aware Captioners. - Bo Wan; Michael Tschannen; Yongqin Xian; Filip Pavetic; Ibrahim M. Alabdulmohsin; Xiao Wang 038; André Susano Pinto; Andreas Steiner 001; Lucas Beyer; Xiaohua Zhai (NeurIPS, 2024)

PaliGemma: A versatile 3B VLM for transfer. - Lucas Beyer; Andreas Steiner 001; André Susano Pinto; Alexander Kolesnikov 003; Xiao Wang 038; Daniel Salz; Maxim Neumann; Ibrahim Alabdulmohsin; Michael Tschannen; Emanuele Bugliarello; Thomas Unterthiner; Daniel Keysers; et al. (CoRR, 2024)

PaliGemma 2: A Family of Versatile VLMs for Transfer. - Andreas Steiner 001; André Susano Pinto; Michael Tschannen; Daniel Keysers; Xiao Wang 038; Yonatan Bitton; Alexey A. Gritsenko; Matthias Minderer; Anthony Sherbondy; Shangbang Long; Siyang Qin; R. Reeve Ingle; et al. (CoRR, 2024)

Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. - Aleksandar Stanic; Sergi Caelles; Michael Tschannen (Trans. Mach. Learn. Res., 2024)

JetFormer: An autoregressive generative model of raw images and text. - Michael Tschannen; André Susano Pinto; Alexander Kolesnikov 003 (ICLR, 2025)

Jet: A Modern Transformer-Based Normalizing Flow. - Alexander Kolesnikov 003; André Susano Pinto; Michael Tschannen (Trans. Mach. Learn. Res., 2025)

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features. - Michael Tschannen; Alexey A. Gritsenko; Xiao Wang 038; Muhammad Ferjad Naeem; Ibrahim Alabdulmohsin; Nikhil Parthasarathy; Talfan Evans; Lucas Beyer; Ye Xia; Basil Mustafa; Olivier J. Hénaff; Jeremiah Harmsen; et al. (CoRR, 2025)

Quantization-Free Autoregressive Action Transformer. - Ziyad Sheebaelhamd; Michael Tschannen; Michael Muehlebach; Claire Vernade (CoRR, 2025)