Dominik Dold

According to our database, Dominik Dold authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Towards Large-scale Network Emulation on Analog Neuromorphic Hardware.

Elias Arnold, Philipp Spilger, Jan V. Straub, Eric Müller, Dominik Dold, Gabriele Meoni, Johannes Schemmel

CoRR, 2024

2023

Differentiable graph-structured models for inverse design of lattice materials.

Dominik Dold, Derek Aranguren van Egmond

CoRR, 2023

2022

Selected Trends in Artificial Intelligence for Space Applications.

Dario Izzo, Gabriele Meoni, Pablo Gómez, Dominik Dold, Alexander Zoechbauer

CoRR, 2022

Neuromorphic Computing and Sensing in Space.

Dario Izzo, Alexander Hadjiivanov, Dominik Dold, Gabriele Meoni, Emmanuel Blazquez

CoRR, 2022

Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods.

Anna Himmelhuber, Dominik Dold, Stephan Grimm, Sonja Zillner, Thomas A. Runkler

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2022

Relational representation learning with spike trains.

Dominik Dold

Proceedings of the International Joint Conference on Neural Networks, 2022

Neuro-symbolic computing with spiking neural networks.

Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas A. Runkler

Proceedings of the ICONS 2022: International Conference on Neuromorphic Systems, Knoxville, TN, USA, July 27, 2022

2021

Fast and energy-efficient neuromorphic deep learning with first-spike times.

Julian Göltz, Laura Kriener, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Benjamin Cramer, Dominik Dold, Ákos F. Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

Nat. Mach. Intell., 2021

Learning through structure: towards deep neuromorphic knowledge graph embeddings.

Victor Caceres Chian, Marcel Hildebrandt, Thomas A. Runkler, Dominik Dold

CoRR, 2021

SpikE: spike-based embeddings for multi-relational graph data.

Dominik Dold, Josep Soler Garrido

Proceedings of the International Joint Conference on Neural Networks, 2021

An energy-based model for neuro-symbolic reasoning on knowledge graphs.

Dominik Dold, Josep Soler Garrido

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

Machine learning on knowledge graphs for context-aware security monitoring.

Josep Soler Garrido, Dominik Dold, Johannes Frank

Proceedings of the IEEE International Conference on Cyber Security and Resilience, 2021

2020

Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates

Dominik Dold

PhD thesis, 2020

Live Demonstration: Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate.

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Ákos Ferenc Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Gerd Kiene, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier

Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate.

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Ákos Ferenc Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Gerd Kiene, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier

Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

2019

Stochasticity from function - Why the Bayesian brain may need no noise.

Dominik Dold, Ilja Bytschok, Ákos F. Kungl, Andreas Baumbach, Oliver Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

Neural Networks, 2019

Fast and deep neuromorphic learning with time-to-first-spike coding.

Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Dominik Dold, Laura Kriener, Ákos Ferenc Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

CoRR, 2019

2018

Generative models on accelerated neuromorphic hardware.

Ákos F. Kungl, Sebastian Schmitt, Johann Klähn, Paul Müller, Andreas Baumbach, Dominik Dold, Alexander Kugele, Nico Gürtler, Luziwei Leng, Eric Müller, Christoph Koke, Mitja Kleider, Christian Mauch, Oliver Breitwieser, Maurice Güttler, Dan Husmann de Oliveira, Kai Husmann, Joscha Ilmberger, Andreas Hartel, Vitali Karasenko, Andreas Grübl, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

CoRR, 2018