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“Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation” accepted at WACV 2024

Our paper title “Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation” has been accepted for publication at the Winter Conference on Applications of Computer Vision (WACV). The paper tackles the challenging real-world task to semantically segment cracks in concrete infrastructure.… Read More »“Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation” accepted at WACV 2024

Continual Causality PMLR proceedings & retrospective paper are out

The proceedings, Proceedings of Machine Learning (PMLR) Volume 208, are now publicly available for the AAAI-23 Continual Causality bridge (https://www.continualcausality.org). Proceedings include close to a 100 pages of content that initiates a first bridge between continual learning & causality research. As a special foreword, the organizers… Read More »Continual Causality PMLR proceedings & retrospective paper are out

“Probabilistic Circuits That Know What They Don’t Know” accepted with oral presentation at UAI 2023

Our paper titled “Probabilistic Circuits That Know What They Don’t Know” has been accepted for publication at the Uncertainty in Artificial Intelligence (UAI) conference 2023 and has been selected for a 25 minute oral presentation. The paper draws inspiration from the overconfidence phenomenon in neural… Read More »“Probabilistic Circuits That Know What They Don’t Know” accepted with oral presentation at UAI 2023

“Queer in AI: A Case Study in Community-led Participatory AI” accepted at ACM FAccT 2023

In collaboration with the Queer in AI community, a paper titled “Queer in AI: A Case Study in Community-led Participatory AI” has been accepted for publication at the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2023. As implied in the title, the paper… Read More »“Queer in AI: A Case Study in Community-led Participatory AI” accepted at ACM FAccT 2023

Invited talk at SMSI 2023: “Continually Learning Machines That Understand What They Don’t Know”

Martin Mundt has given an invited talk at the Sensor and Measurement Science International Conference 2023 (SMSI) As sensors and measurement may drift in practice for a variety of reasons, it is important that intelligent systems are equipped with respective capabilities to recognize and update… Read More »Invited talk at SMSI 2023: “Continually Learning Machines That Understand What They Don’t Know”