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Different architectures for neural ordinary differential equations

By Elias Walder

Different architectures for neural ordinary differential equations

By Elias Walder

0 - Default Title
Description
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.
Product details
Binding:
Paperback
Number of Pages:
96
Release Date:
2025-07-17
Publication Date:
2025-07-17
Publisher:
LAP LAMBERT Academic Publishing
Languages:
Original: English
ISBN10:
6207486439
ISBN13:
9786207486434
Weight:
161 g
Height:
150 cm
Width:
220 cm
Thickness:
6 cm
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