Search Results for author: Frederik Diehl

Found 9 papers, 3 papers with code

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

1 code implementation14 Jun 2019 Thomas Brunner, Frederik Diehl, Alois Knoll

Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail.

valid

Edge Contraction Pooling for Graph Neural Networks

no code implementations27 May 2019 Frederik Diehl

Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers.

General Classification Graph Classification

Leveraging Semantic Embeddings for Safety-Critical Applications

no code implementations19 May 2019 Thomas Brunner, Frederik Diehl, Michael Truong Le, Alois Knoll

Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning.

Zero-Shot Learning

Graph Neural Networks for Modelling Traffic Participant Interaction

no code implementations4 Mar 2019 Frederik Diehl, Thomas Brunner, Michael Truong Le, Alois Knoll

We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account.

Traffic Prediction

Traceability of Deep Neural Networks

no code implementations17 Dec 2018 Vincent Aravantinos, Frederik Diehl

We investigate which artifacts could play a similar role to code or low-level requirements in neural network development and propose various traces which one could possibly consider as a replacement for classical notions.

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

no code implementations4 Sep 2017 Chih-Hong Cheng, Frederik Diehl, Yassine Hamza, Gereon Hinz, Georg Nührenberg, Markus Rickert, Harald Ruess, Michael Troung-Le

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards.

ML-based tactile sensor calibration: A universal approach

no code implementations21 Jun 2016 Maximilian Karl, Artur Lohrer, Dhananjay Shah, Frederik Diehl, Max Fiedler, Saahil Ognawala, Justin Bayer, Patrick van der Smagt

We study the responses of two tactile sensors, the fingertip sensor from the iCub and the BioTac under different external stimuli.

apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters

1 code implementation10 Mar 2015 Frederik Diehl, Andreas Jauch

The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer.

BIG-bench Machine Learning Hyperparameter Optimization

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