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Books and Theses
- 2024
[b1]Julian Stier:
Structure of Artificial Neural Networks: Empirical Investigations. University of Passau, Germany, 2024
Journal Articles
- 2022
- [j2]Julian Stier
, Michael Granitzer:
deepstruct - linking deep learning and graph theory. Softw. Impacts 11: 100193 (2022) - 2021
- [j1]Clemens Havas, Lorenz Wendlinger, Julian Stier, Sahib Julka, Veronika Krieger, Cornelia Ferner, Andreas Petutschnig, Michael Granitzer, Stefan Wegenkittl, Bernd Resch:
Spatio-Temporal Machine Learning Analysis of Social Media Data and Refugee Movement Statistics. ISPRS Int. J. Geo Inf. 10(8): 498 (2021)
Conference and Workshop Papers
- 2024
- [c8]Simon Neumeyer, Julian Stier, Michael Granitzer:
Efficient NAS with FaDE on Hierarchical Spaces. IDA (2) 2024: 158-170 - 2023
- [c7]Ousmane Touat, Julian Stier, Pierre-Edouard Portier, Michael Granitzer:
GRAN Is Superior to GraphRNN: Node Orderings, Kernel- and Graph Embeddings-Based Metrics for Graph Generators. LOD (1) 2023: 430-444 - 2021
- [c6]Lorenz Wendlinger, Julian Stier, Michael Granitzer:
Evofficient: Reproducing a Cartesian Genetic Programming Method. EuroGP 2021: 162-178 - [c5]Julian Stier, Michael Granitzer:
DeepGG: A Deep Graph Generator. IDA 2021: 313-324 - [c4]Mehdi Ben Amor, Julian Stier, Michael Granitzer:
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors. KES 2021: 4073-4082 - [c3]Julian Stier, Harshil Darji, Michael Granitzer:
Experiments on Properties of Hidden Structures of Sparse Neural Networks. LOD 2021: 380-394 - 2019
- [c2]Julian Stier, Michael Granitzer:
Structural Analysis of Sparse Neural Networks. KES 2019: 107-116 - 2018
- [c1]Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler:
Analysing Neural Network Topologies: a Game Theoretic Approach. KES 2018: 234-243
Informal and Other Publications
- 2024
- [i9]Simon Neumeyer, Julian Stier, Michael Granitzer:
Efficient NAS with FaDE on Hierarchical Spaces. CoRR abs/2404.16218 (2024) - [i8]Julian Stier:
Structure of Artificial Neural Networks - Empirical Investigations. CoRR abs/2410.09579 (2024) - 2023
- [i7]Ousmane Touat, Julian Stier, Pierre-Edouard Portier, Michael Granitzer:
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators. CoRR abs/2307.06709 (2023) - 2021
- [i6]Mehdi Ben Amor, Julian Stier, Michael Granitzer:
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors. CoRR abs/2107.06158 (2021) - [i5]Julian Stier, Harshil Darji, Michael Granitzer:
Experiments on Properties of Hidden Structures of Sparse Neural Networks. CoRR abs/2107.12917 (2021) - [i4]Julian Stier, Michael Granitzer:
deepstruct - linking deep learning and graph theory. CoRR abs/2111.06679 (2021) - 2020
- [i3]Julian Stier, Michael Granitzer:
Deep Graph Generators. CoRR abs/2006.04159 (2020) - 2019
- [i2]Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler:
Analysing Neural Network Topologies: a Game Theoretic Approach. CoRR abs/1904.08166 (2019) - [i1]Julian Stier, Michael Granitzer:
Structural Analysis of Sparse Neural Networks. CoRR abs/1910.07225 (2019)
Coauthor Index
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