269 citations · 349 across the 5 of their papers we have counts for
5 papers
Deep recurrent Gaussian process with variational Sparse Spectrum approximation
Roman Föll, Bernard Haasdonk, Markus Hanselmann +1
Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems, s…
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
Markus Hanselmann, Thilo Strauss, Katharina Dormann +1
We propose a novel neural network architecture for detecting intrusions on the CAN bus. The Controller Area Network (CAN) is the standard communication method between the Electroni…
Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks
Andrej Junginger, Markus Hanselmann, Thilo Strauss +3
Neural networks have proven their capabilities by outperforming many other approaches on regression or classification tasks on various kinds of data. Other astonishing results have…
Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation
Roman Föll, Bernard Haasdonk, Markus Hanselmann +1
Modeling sequential data has become more and more important in practice. Some applications are autonomous driving, virtual sensors and weather forecasting. To model such systems so…
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
Thilo Strauss, Markus Hanselmann, Andrej Junginger +1
Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable…