Dominik Dold
Dominik Dold
Research Fellow, ESA ESTEC
Verified email at - Homepage
Cited by
Cited by
Fast and energy-efficient neuromorphic deep learning with first-spike times
J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ...
Nature machine intelligence 3 (9), 823-835, 2021
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
S Billaudelle, Y Stradmann, K Schreiber, B Cramer, A Baumbach, D Dold, ...
2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2020
Accelerated physical emulation of bayesian inference in spiking neural networks
AF Kungl, S Schmitt, J Klähn, P Müller, A Baumbach, D Dold, A Kugele, ...
Frontiers in neuroscience 13, 1201, 2019
Stochasticity from function—why the bayesian brain may need no noise
D Dold, I Bytschok, AF Kungl, A Baumbach, O Breitwieser, W Senn, ...
Neural networks 119, 200-213, 2019
Machine learning on knowledge graphs for context-aware security monitoring
JS Garrido, D Dold, J Frank
2021 IEEE International Conference on Cyber Security and Resilience (CSR), 55-60, 2021
Lagrangian dynamics of dendritic microcircuits enables real-time backpropagation of errors
D Dold, AF Kungl, J Sacramento, MA Petrovici, K Schindler, J Binas, ...
Cosyne Abstracts 2019, 2019
An energy-based model for neuro-symbolic reasoning on knowledge graphs
D Doldy, JS Garridoy
2021 20th IEEE International Conference on Machine Learning and Applications …, 2021
Selected Trends in Artificial Intelligence for Space Applications
D Izzo, G Meoni, P Gómez, D Dold, A Zoechbauer
arXiv preprint arXiv:2212.06662, 2022
Spike: Spike-based embeddings for multi-relational graph data
D Dold, JS Garrido
2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021
Neuromorphic Computing and Sensing in Space
D Izzo, A Hadjiivanov, D Dold, G Meoni, E Blazquez
arXiv preprint arXiv:2212.05236, 2022
Neuro-symbolic computing with spiking neural networks
D Dold, J Soler Garrido, V Caceres Chian, M Hildebrandt, T Runkler
Proceedings of the International Conference on Neuromorphic Systems 2022, 1-4, 2022
Learning through structure: towards deep neuromorphic knowledge graph embeddings
VC Chian, M Hildebrandt, T Runkler, D Dold
2021 International Conference on Neuromorphic Computing (ICNC), 61-70, 2021
Differentiable graph-structured models for inverse design of lattice materials
D Dold, DA van Egmond
arXiv preprint arXiv:2304.05422, 2023
Relational representation learning with spike trains
D Dold
2022 International Joint Conference on Neural Networks (IJCNN), 2022
Deep reinforcement learning in a time-continuous model
AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici
Bernstein Conference, doi 10, 2019
Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates
D Dold
Advance Concepts Team
D Izzo, G Healy, B Addis, BL Bentley, M Märtens, M Casasco, C Bramanti, ...
European Space Agency, 2004
A neuronal least-action principle for real-time learning in cortical circuits
W Senn, D Dold, AF Kungl, B Ellenberger, J Jordan, Y Bengio, ...
bioRxiv, 2023.03. 25.534198, 2023
Evaluating the feasibility of interpretable machine learning for globular cluster detection
D Dold, K Fahrion
Astronomy & Astrophysics (A&A) 663, A81, 2022
Method and system for anomaly detection in a network
Y Liu, M Joblin, M Hildebrandt, D Dold
US Patent App. 18/138,197, 2023
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