79 citations · 218 across the 22 of their papers we have counts for
4 papers · 1 filter
deepstruct -- linking deep learning and graph theory
Julian Stier, Michael Granitzer
deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained n…
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Julian Stier, Harshil Darji, Michael Granitzer
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning…
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors
M. Ben Amor, J. Stier, M. Granitzer
Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performan…
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
Sahib Julka, Vishal Sowrirajan, Joerg Schloetterer +1
Motion behaviour is driven by several factors -- goals, presence and actions of neighbouring agents, social relations, physical and social norms, the environment with its variable…