Tutorials for (deep convolutional) neural networks.
A two-layer perceptron for digit classification on MNIST implemented in MatLab.
In the course of a seminar on “Selected Topics in Image Processing”, I worked on iPiano, an algorithm for non-convex and non-smooth optimization proposed by Ochs et al. . iPiano combines forward-backward splitting with an inertial force. This article presents the corresponding seminar paper including an implementation in C++ with applications to image denoising, image segmentation and compressed sensing.
Efficient C++ implementation of iPiano, a proximal algorithm with inertial force for non-convex and non-smooth optimization; including applications to image segmentation.
In the course of my second seminar on “Current Topics in Computer Vision and Machine Learning”, offered by the Computer Vision Group at RWTH Aachen University, I wrote a report entitled “Neural Codes for Image Retrieval”. The work is motivated by recent research by Bebanko et al.  and the report as well as the corresponding slides can be found here.
In my sixth semester at RWTH Aachen University, I am currently attending a seminar offered by the Computer Vision Group headed by Prof. Leibe on “Current Topics in Computer Vision and Machine Learning”. In the course of this seminar, I wrote a seminar paper entitled “Understanding Convolutional Neural Networks”. Both the seminar paper as well as the slides of the corresponding talk can be found here.
During my fifth semester at RWTH Aachen University I participated in a seminar offered by the Chair of Computer Science 6 on “Selected Topics in Human Language Technology and Pattern Recognition”. As result, I wrote a seminar paper on neural networks for pattern recognition titled “Introduction to Neural Networks”. At the end of the seminar, all participants had to give a short talk on their topic. Both my seminar paper and my slides of the talk can be found here.