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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Are LMMs Robust to Small Image Perturbations?
Published:
In this blog post we investigate whether Large Multimodal Models (LMMs) are robust to small perturbations of input images. We find that this is not the case, we manage to fool Phi-4-multimodal-instruct by small changes to its inputs.
Hard Mining for Robust Classification
Published:
In this blog post we want to explore whether training mostly on the hardest examples allows us to fit robust networks on CIFAR-10 quicker.
1-Lipschitz Layers Compared
Published:
In our recent CVPR paper, “1-Lipschitz Layers Compared: Memory, Speed and Certifiable Robustness” we compared different methods of creating 1-Lipschitz convolutions. In this blog post we try to give some additional background on why 1-Lipschitz methods are an interesting research topic, and discuss some results from the paper.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
Published in ECCV, 2022
We introduce an efficient rescaling-based layer that allows us to train state-of-the-art certifiably robust image classifiers.
1-Lipschitz Neural Networks are more expressive with N-Activations
Published in arXiv, 2023
We show a shortcoming with currently popular activation functions in 1-Lipschitz networks and propose an activation function that provably overcomes this limitation.
1-Lipschitz Layers Compared: Memory, Speed and Certifiable Robustness
Published in CVPR, 2024
A large scale comparison of methods of creating 1-Lipschitz convolutions, considering both theoretical properties as well as experimental results.
Intriguing Properties of Robust Classification
Published in CVPR-workshops, 2025
Despite plenty of research in the last 10 years, we have made limited progress towards generating robust machine learning models. Therefore, in this paper we explore the question of whether current datasets are large enough to train robust image classifiers.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.