Different architectures for neural ordinary differential equations - Walder, Elias
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Machine learning has been getting more and more important during the last decades. One of the most important tools in machine learning are neural networks. A rather modern approach of constructing a neural network is using a neural ordinary differential equation (or neural ODE). Here, the idea is to construct a neural network which can be evaluated by (numerically) solving an ODE. Neural ODEs are a powerful tool to solve many different machine learning problems. However, it is not so easy to construct a fitting neural ODE model in practice. In the thesis, some basic ways of constructing a neural ODE are explored.…mehr

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Produktbeschreibung
Machine learning has been getting more and more important during the last decades. One of the most important tools in machine learning are neural networks. A rather modern approach of constructing a neural network is using a neural ordinary differential equation (or neural ODE). Here, the idea is to construct a neural network which can be evaluated by (numerically) solving an ODE. Neural ODEs are a powerful tool to solve many different machine learning problems. However, it is not so easy to construct a fitting neural ODE model in practice. In the thesis, some basic ways of constructing a neural ODE are explored.
Autorenporträt
Elias Walder, BSc, Dipl.-Ing., studied at the University of Innsbruck, Faculty of Mathematics, Computer Science and Physics. His academic focus lies in applied mathematics.