Matthias Kirchler

According to our database, Matthias Kirchler authored at least 11 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

On the Challenges and Opportunities in Generative AI.

Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin

CoRR, 2024

MixerFlow: MLP-Mixer Meets Normalising Flows.

Eshant English, Matthias Kirchler, Christoph Lippert

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Kernelised Normalising Flows.

Eshant English, Matthias Kirchler, Christoph Lippert

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

MixerFlow for Image Modelling.

Eshant English, Matthias Kirchler, Christoph Lippert

CoRR, 2023

Training Normalizing Flows from Dependent Data.

Matthias Kirchler, Christoph Lippert, Marius Kloft

Proceedings of the International Conference on Machine Learning, 2023

2022

transferGWAS: GWAS of images using deep transfer learning.

Matthias Kirchler, Stefan Konigorski, Matthias Norden, Christian Meltendorf, Marius Kloft, Claudia Schurmann, Christoph Lippert

Bioinform., 2022

ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics.

Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Explainability Requires Interactivity.

Matthias Kirchler, Martin Graf, Marius Kloft, Christoph Lippert

CoRR, 2021

2020

Two-sample Testing Using Deep Learning.

Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2016

Behavior-based tracking of Internet users with semi-supervised learning.

Dominik Herrmann, Matthias Kirchler, Jens Lindemann, Marius Kloft

Proceedings of the 14th Annual Conference on Privacy, Security and Trust, 2016

Tracked Without a Trace: Linking Sessions of Users by Unsupervised Learning of Patterns in Their DNS Traffic.

Matthias Kirchler, Dominik Herrmann, Jens Lindemann, Marius Kloft

Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, 2016