Decorating

Deep Conceptors for Temporal Data Mining

With this project, we provide the software for LRNNs (linear recurrent neural networks). The software has been implemented by the authors in Python and the scientific programming language Octave (see octave.org, version 5.2.0). It has been tested under Linux. The main routines can be found in lrnn implemented in Octave. For further details of LRNNs and their properties, the interested reader is referred to the following paper.

Attributions

The relevant paper for the software and case studies is at http://arxiv.org/abs/1802.03308:

Frieder Stolzenburg, Sandra Litz, Olivia Michael und Oliver Obst. The power of linear recurrent neural networks. CoRR – Computing Research Repository abs/1802.03308, Cornell University Library, 2018. Latest revision 2023.

Acknowledgements

The research reported here has been supported by the German Academic Exchange Service (DAAD) by funds of the German Federal Ministry of Education and Research (BMBF) in the Programmes for Project-Related Personal Exchange (PPP) under grant no. 57319564 and Universities Australia (UA) in the Australia-Germany Joint Research Cooperation Scheme within the project Deep Conceptors for Temporal Data Mining (Decorating).

Licenses

The code that we make available with the Decorating project is released under BSD-3-Clause License: https://opensource.org/license/bsd-3-clause/