Daniel Gedon

Daniel Gedon
Uppsala University | UU · Department of Information Technology

Master of Science

About

10
Publications
1,961
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32
Citations
Citations since 2016
10 Research Items
32 Citations
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2016201720182019202020212022024681012
2016201720182019202020212022024681012
Introduction
Daniel Gedon is a PhD candidate in the division of System and Control, at Uppsala University, Sweden. His research focuses on understanding deep learning and applying it to medical classification problems.

Publications

Publications (10)
Article
Full-text available
Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We...
Preprint
Full-text available
Myocardial infarction diagnosis is a common challenge in the emergency department. In managed settings, deep learning-based models and especially convolutional deep models have shown promise in electrocardiogram (ECG) classification, but there is a lack of high-performing models for the diagnosis of myocardial infarction in real-world scenarios. We...
Preprint
Full-text available
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. The use of deep SSMs as a black-box identification model can describe a wide range of dynamics due to the flexibility of deep neural networks. Additionally, the probabilist...
Article
Full-text available
In recent years, machine-learning methods have become increasingly important for the experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger systems to reconstruction and data analysis. The recent UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily mo...
Article
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. The use of deep SSMs as a black-box identification model can describe a wide range of dynamics due to the flexibility of deep neural networks. Additionally, the probabilist...
Conference Paper
Full-text available
An extension of the Tensor Network (TN) Kalman filter [2], [3] for large scale LTI systems is presented in this paper. The TN Kalman filter can handle exponentially large state vectors without constructing them explicitly. In order to have efficient algebraic operations, a low TN rank is required. We exploit the possibility to approximate the covar...
Conference Paper
Full-text available
In 2011 ESA published the ESA Pointing Error Engineering Handbook as applicable document. The Handbook complements the ECSS control performance standard. It provides guidelines for a step-by-step engineering process from pointing error requirement specification, to systematic pointing error analysis, and the compilation of pointing error budgets. A...

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