no code implementations • 2 May 2024 • Lena Todnem Bach Christensen, Dikte Straadt, Stratos Vassis, Christian Marius Lillelund, Peter Bangsgaard Stoustrup, Ruben Pauwels, Thomas Klit Pedersen, Christian Fischer Pedersen
The results show promise for an AI model in the assessment of TMJ involvement in children and as a decision support tool.
1 code implementation • 2 May 2024 • Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen, Manuel Morante, Christian Fischer Pedersen
Our work encourages the inclusion of censored data in predictive maintenance models and highlights the unique advantages that survival analysis offers when it comes to probabilistic RUL estimation and early fault detection.
no code implementations • 9 Apr 2024 • Christian Marius Lillelund, Martin Magris, Christian Fischer Pedersen
In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.
1 code implementation • 13 Sep 2023 • Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen, Christian Fischer Pedersen
In this paper, we propose a novel approach to predict the time to failure in ball bearings using survival analysis.
no code implementations • 24 Mar 2023 • Jimmi Agerskov, Kristian Nielsen, Christian Marius Lillelund, Christian Fischer Pedersen
We produce a fully-functional prototype that can retrieve, cluster and present information about cancer trajectories from non-clinical forum posts.
1 code implementation • 1 Mar 2023 • Christian Marius Lillelund, Henrik Bagger Jensen, Christian Fischer Pedersen
Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data.
1 code implementation • 12 Oct 2021 • Friedrich Dörmann, Osvald Frisk, Lars Nørvang Andersen, Christian Fischer Pedersen
In this study, we show that these two types of noise are equivalent in their effect on the utility of private neural networks, however they are not accounted for equally in the privacy budget.
no code implementations • 18 Mar 2021 • Osvald Frisk, Friedrich Dörmann, Christian Marius Lillelund, Christian Fischer Pedersen
The combination of deep neural networks and Differential Privacy has been of increasing interest in recent years, as it offers important data protection guarantees to the individuals of the training datasets used.