# Posts by Collection

## FTPnet

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

## Volume Mixer Plus

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

## NNCreator

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

## Data Viewer

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

## Keras Utility & Layer Collection

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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.

## Show, Attend and Tell for Keras

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Keras implementation of the paper Show, Attend and Tell.

## Transformer: Attention is all you need for Keras

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Implementation of the Transformer architecture described by Vaswani et al. in Attention Is All You Need.

## Lidar Viewer

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

## EmoMatch

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

## VGGVox for PyTorch

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Implementation of the VGGVox network in PyTorch.

## Bayesian Spiking Neural Networks

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

## Faster Training of Mask R-CNN by Focusing on Instance Boundaries

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Reference implementation of Faster Training of Mask R-CNN by Focusing on Instance Boundaries.

## Portfolio item number 1

Short description of portfolio item number 1

## Portfolio item number 2

Short description of portfolio item number 2

## Faster Training of Mask R-CNN by Focusing on Instance Boundaries

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

## 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.

## Talk 1 on Relevant Topic in Your Field

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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

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