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Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks


Jun 14, 2019
Thomas Brunner, Frederik Diehl, Alois Knoll

* Presented at CVPR 2019 Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems 

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Edge Contraction Pooling for Graph Neural Networks


May 27, 2019
Frederik Diehl


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Leveraging Semantic Embeddings for Safety-Critical Applications


May 19, 2019
Thomas Brunner, Frederik Diehl, Michael Truong Le, Alois Knoll

* Accepted at CVPR 2019 Workshop: Safe Artificial Intelligence for Automated Driving 

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Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving


May 08, 2019
Tobias Kessler, Julian Bernhard, Martin Buechel, Klemens Esterle, Patrick Hart, Daniel Malovetz, Michael Truong Le, Frederik Diehl, Thomas Brunner, Alois Knoll

* Accepted at IEEE Intelligent Vehicles Symposium (IV), 2019 

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Graph Neural Networks for Modelling Traffic Participant Interaction


Mar 04, 2019
Frederik Diehl, Thomas Brunner, Michael Truong Le, Alois Knoll


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Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks


Dec 24, 2018
Thomas Brunner, Frederik Diehl, Michael Truong Le, Alois Knoll


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Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives


Sep 04, 2017
Chih-Hong Cheng, Frederik Diehl, Yassine Hamza, Gereon Hinz, Georg Nührenberg, Markus Rickert, Harald Ruess, Michael Troung-Le

* Summary for activities conducted in the fortiss Eigenforschungsprojekt "TdpSW - Towards dependable and predictable SW for ML-based autonomous systems". All ANN-based motion predictors being formally analyzed are available in the source file 

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ML-based tactile sensor calibration: A universal approach


Jun 21, 2016
Maximilian Karl, Artur Lohrer, Dhananjay Shah, Frederik Diehl, Max Fiedler, Saahil Ognawala, Justin Bayer, Patrick van der Smagt


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apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters


Mar 15, 2015
Frederik Diehl, Andreas Jauch


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