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Implementation of a FTP client in .NET 3.5 for desktop and portable applications.
Improves the default Windows Volume Mixer to allows one to change the default playback device by using shortcuts.
GUI to create network graphs for the CNTK framework, which afterwards can be trained and evaluated in C++.
Utility tool to inspect various data formats to verify data integrity in machine learning.
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.
Keras implementation of the paper
Show, Attend and Tell.
Implementation of the Transformer architecture described by Vaswani et al. in
Attention Is All You Need.
Point cloud viewer with surface reconstruction for LIDAR data using OpenGL.
Unsupervised Audio + Video Network Pretraining using PyTorch based on the correlation between audio and video signal.
Implementation of the VGGVox network in PyTorch.
Implementation of the paper
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints by Habenschuss et al.
Reference implementation of
Faster Training of Mask R-CNN by Focusing on Instance Boundaries.
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Published in University of Göttingen, 2017
Examination of recurrent neural networks (Echo State Networks) for the multiple prediction tasks in chaotic systems.
Zimmermann, Roland S. (2017). Prediction spatio-temporal dynamics using reservoir computing.https://github.com/FlashTek/rcp_spatio_temporal/raw/master/paper/thesis/latex/thesis.pdf
Published in arXiv, 2018
Improving the training of Mask R-CNN for instance segmentation by introducing an intuitive auxiliary loss.
Zimmermann, R. S. and Siems, J. N. (2018). Faster Training of Mask R-CNN by Focusing on Instance Boundaries. arXiv preprint arXiv:1809.07069.https://arxiv.org/abs/1809.07069
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
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.
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.