29 citations · 48 across the 9 of their papers we have counts for
9 papers
Structure of Artificial Neural Networks -- Empirical Investigations
Julian Stier
Within one decade, Deep Learning overtook the dominating solution methods of countless problems of artificial intelligence. ``Deep'' refers to the deep architectures with operation…
Efficient NAS with FaDE on Hierarchical Spaces
Simon Neumeyer, Julian Stier, Michael Granitzer
Neural architecture search (NAS) is a challenging problem. Hierarchical search spaces allow for cheap evaluations of neural network sub modules to serve as surrogate for architectu…
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
Ousmane Touat, Julian Stier, Pierre-Edouard Portier +1
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating gra…
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…
DeepGG: a Deep Graph Generator
Julian Stier, Michael Granitzer
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning…
Structural Analysis of Sparse Neural Networks
Julian Stier, Michael Granitzer
Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages. We give motivation to study Artificial Neural Networks (ANNs) from a…
Analysing Neural Network Topologies: a Game Theoretic Approach
Julian Stier, Gabriele Gianini, Michael Granitzer +1
Artificial Neural Networks have shown impressive success in very different application cases. Choosing a proper network architecture is a critical decision for a network's success,…