Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Different architectures for neural ordinary differential equations

Different architectures for neural ordinary differential equations

Walder, Elias

Paperback

General Mathematics

ISBN10: 6207486439
ISBN13: 9786207486434
Publisher: LAP Lambert Academic Publishing
Published: Jul 17 2025
Pages: 96
Weight: 0.31
Height: 0.23 Width: 6.00 Depth: 9.00
Language: English
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.

Also in

General Mathematics

View All