Jannis Schuecker
Jannis Schuecker
Research Fellow at Fraunhofer IAIS
Verified email at iais.fraunhofer.de
Title
Cited by
Cited by
Year
Informed Machine Learning--A Taxonomy and Survey of Integrating Knowledge into Learning Systems
L von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ...
arXiv preprint arXiv:1903.12394, 2019
612019
Optimal sequence memory in driven random networks
J Schuecker, S Goedeke, M Helias
arXiv preprint arXiv:1603.01880, 2016
442016
Nest 2.12. 0
S Kunkel, R Deepu, HE Plesser, B Golosio, ME Lepperød, JM Eppler, ...
Jülich Supercomputing Center, 2017
362017
Modulated escape from a metastable state driven by colored noise
J Schuecker, M Diesmann, M Helias
Physical Review E 92 (5), 052119, 2015
352015
Informed machine learning–towards a taxonomy of explicit integration of knowledge into machine learning
L Von Rueden, S Mayer, J Garcke, C Bauckhage, J Schuecker
learning 18, 19-20, 2019
322019
Fundamental activity constraints lead to specific interpretations of the connectome
J Schuecker, M Schmidt, SJ van Albada, M Diesmann, M Helias
PLoS computational biology 13 (2), e1005179, 2017
292017
Integration of continuous-time dynamics in a spiking neural network simulator
J Hahne, D Dahmen, J Schuecker, A Frommer, M Bolten, M Helias, ...
Frontiers in neuroinformatics 11, 34, 2017
282017
NEST 2.14. 0
A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ...
Jülich Supercomputing Center, 2017
202017
Functional methods for disordered neural networks
J Schücker, S Goedeke, D Dahmen, M Helias
arXiv preprint arXiv:1605.06758, 2016
172016
Noise dynamically suppresses chaos in neural networks
S Goedeke, J Schuecker, M Helias
arXiv preprint arXiv.1603.01880, 2016
102016
Leveraging domain knowledge for reinforcement learning using mmc architectures
R Ramamurthy, C Bauckhage, R Sifa, J Schücker, S Wrobel
International Conference on Artificial Neural Networks, 595-607, 2019
92019
NEST 2.8. 0
JM Eppler, R Deepu, C Bachmann, T Zito, A Peyser, J Jordan, R Pauli, ...
JARA-HPC, 2015
82015
Conditions for traveling waves in spiking neural networks
J Senk, K Korvasová, J Schuecker, E Hagen, T Tetzlaff, M Diesmann, ...
arXiv preprint arXiv:1801.06046, 2018
72018
Informed Machine Learning Through Functional Composition.
C Bauckhage, C Ojeda, J Schücker, R Sifa, S Wrobel
LWDA, 33-37, 2018
42018
Noise dynamically suppresses chaos in random neural networks
S Goedeke, J Schuecker, M Helias
arXiv preprint arXiv:1603.01880, 2016
42016
Adiabatic Quantum argmax Computation
C Bauckhage, K Cvejoski, C Ojeda, J Schücker, R Sifa
researchgate, Tech. Rep, 2018
12018
Spectral properties of excitable systems subject to colored noise
J Schuecker, M Diesmann, M Helias
arXiv preprint arXiv:1411.0432, 2014
12014
Reduction of colored noise in excitable systems to white noise and dynamic boundary conditions
J Schuecker, M Diesmann, M Helias
arXiv preprint arXiv:1410.8799, 2014
12014
Auto Encoding Explanatory Examples with Stochastic Paths
C Ojeda, RJ Sánchez, K Cvejoski, J Schücker, C Bauckhagez, ...
2020 25th International Conference on Pattern Recognition (ICPR), 6219-6226, 2021
2021
Switching Dynamical Systems with Deep Neural Networks
C Ojeda, B Georgiev, K Cvejoski, J Schucker, C Bauckhage, RJ Sánchez
2020 25th International Conference on Pattern Recognition (ICPR), 6305-6312, 2021
2021
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