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Jurasiński, Karol (author)
Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions...
master thesis 2019
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Schalkwijk, Paul (author)
As the use of Networked Control Systems increases, the need for control methods with more efficient network usage also grows. These methods require a more sophisticated way of pre- dicting their traffic, and an approach for this is using a formal modelling approach using Timed Automata. Timed Automata have been used for over 25 years for several...
master thesis 2019
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Plaisant van der Wal, Renzo (author)
Machine learning methods are explored in an attempt to achieve better predictive performance than the legacy rule-based fraud detection systems that are currently used to detect fraudulent car insurance claims. There are two key principles that lead the exploration of machine learning techniques and algorithms in this thesis, namely, the...
master thesis 2018
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Verburg, Floris (author)
For performing technical maintenance, it is important to keep a detailed schedule of resources and temporal constraints. The Resource Constrained Project Scheduling Problem (RCPSP) is a well de- fined scheduling model with both resources and temporal constraints. Precedence Constraint Posting (PCP) is a technique to solve the NP-hard RCPSP...
master thesis 2018
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