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# This CITATION.cff file was generated with cffinit. | ||
# Visit https://bit.ly/cffinit to generate yours today! | ||
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cff-version: 1.2.0 | ||
title: Port-Hamilton Neuronal Network | ||
message: >- | ||
If you use this software, please cite it using the | ||
metadata from this file. | ||
type: software | ||
authors: | ||
- given-names: Daniel | ||
family-names: Albert | ||
orcid: 'https://orcid.org/0000-0003-2878-4284' | ||
identifiers: | ||
- type: url | ||
value: 'https://github.com/esclear/ph-nn' | ||
repository-code: 'https://github.com/esclear/ph-nn' | ||
abstract: >- | ||
This is a TensorFlow implementation of Port-Hamiltonian | ||
Neural Networks and their training based on the methods | ||
described in the paper Port-Hamiltonian Approach to Neural | ||
Network Training by Stefano Massaroli, Michael Poli, | ||
et.al. (arXiv:1909.02702v1 [cs.NE]). | ||
It is written for TensorFlow 2.1.0, but it may work with | ||
future versions as well. A main focus in development was | ||
usability and flexibility. The goal was that this | ||
optimizer works with all regular models, i.e. models to | ||
which other, more popular methods, such as SGD, could be | ||
applied as well. | ||
keywords: | ||
- tensorflow | ||
- neuronal-networks | ||
- python | ||
license: GPL-3.0 | ||
commit: 48b1d12231cb8b33ad9c83a4e4d61c2ac01b3e98 | ||
version: v1.0 | ||
date-released: '2020-07-06' |