Posts by Collection

coding

FTPnet

Published:

Implementation of a FTP client in .NET 3.5 for desktop and portable applications.

Volume Mixer Plus

Published:

Improves the default Windows Volume Mixer to allows one to change the default playback device by using shortcuts.

NNCreator

Published:

GUI to create network graphs for the CNTK framework, which afterwards can be trained and evaluated in C++.

Data Viewer

Published:

Utility tool to inspect various data formats to verify data integrity in machine learning.

Keras Utility & Layer Collection

Published:

Collection of custom layers for Keras which are missing in the main framework. These layers might be useful to reproduce current state-of-the-art deep learning papers using Keras.

Lidar Viewer

Published:

Point cloud viewer with surface reconstruction for LIDAR data using OpenGL.

EmoMatch

Published:

Unsupervised Audio + Video Network Pretraining using PyTorch based on the correlation between audio and video signal.

VGGVox for PyTorch

Published:

Implementation of the VGGVox network in PyTorch.

Bayesian Spiking Neural Networks

Published:

Implementation of the paper Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints by Habenschuss et al.

portfolio

publications

Observing spatio-temporal dynamics of excitable media using reservoir computing

Published in Chaos: An Interdisciplinary Journal of Nonlinear Science, 2018

Examination of recurrent neural networks (Echo State Networks) for the spatio-temporal cross prediction of chaotic systems.

Zimmermann, R. S. and Parlitz, U. (2018). Observing spatio-temporal dynamics of excitable media using reservoir computing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(4), 043118.

https://aip.scitation.org/doi/abs/10.1063/1.5022276

Predicting and Observing Chaotic Dynamics in Excitable Media Using Machine Learning

Published in CinC, 2018

Analysis of chaotic dynamics using sequential and recurrent neural networks and classical Machine Learning methods.

Parlitz, U. and Zimmermann, R. S. , Herzog, S. and Isenseee, J. and Datseris, G. (2018). Predicting and Observing Chaotic Dynamics in Excitable Media Using Machine Learning. CinC 2018, Maastrich.

talks