Skip to content

Implementation and applications of DenseNet with dilated convolutional blocks + parameterization with adversarial training.

License

Notifications You must be signed in to change notification settings

mylonasc/deep-rul-models

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

45 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep learning for remaining useful life prediction

This is a repository that contains some legacy code (mainly tensorflow 2) that corresponds to different experiments performed for remaining useful life prediction.

The code is not supported and it is recommended that future users try to implement the techniques described from scratch. It is only provided as reference, since there was some interest in accessing the code.

How to read this repository

In this repository, there are python files that implement a simple GNN library, functions/scripts/notebooks that are meant to execute experiments, and notebooks that are meant to execute and visualize intermitent results of experiments. There are also some notebooks that are not meant to be read (or understood) and it may be easy to understand which are the ones by simple inspection.

This is to clarify that the bulk of the code and scripts in the repository is meant to be ignored. There are some notebooks that may be of use to researchers which I list in what follows:

Do not hesitate to drop me an email for some "behind the scenes" on advice for implementation. However, I cannot promise to reply in a timely manner since I am not working as a researcher anymore.

About

Implementation and applications of DenseNet with dilated convolutional blocks + parameterization with adversarial training.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages