Sebastian Gerwinn
Sebastian Gerwinn
Bosch Center for AI
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Cited by
Cited by
Inferring decoding strategies from choice probabilities in the presence of correlated variability
RM Haefner, S Gerwinn, JH Macke, M Bethge
Nature neuroscience 16 (2), 235-242, 2013
Modeling options for demand side participation of thermostatically controlled loads
M Kamgarpour, C Ellen, SEZ Soudjani, S Gerwinn, JL Mathieu, N Müllner, ...
2013 IREP Symposium Bulk Power System Dynamics and Control-IX Optimization …, 2013
Bayesian inference for generalized linear models for spiking neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in computational neuroscience, 12, 2010
Reassessing optimal neural population codes with neurometric functions
P Berens, AS Ecker, S Gerwinn, AS Tolias, M Bethge
Proceedings of the National Academy of Sciences 108 (11), 4423-4428, 2011
In all likelihood, deep belief is not enough
L Theis, S Gerwinn, F Sinz, M Bethge
The Journal of Machine Learning Research 12, 3071-3096, 2011
Bayesian inference for sparse generalized linear models
M Seeger, S Gerwinn, M Bethge
European Conference on Machine Learning, 298-309, 2007
Bayesian inference for spiking neuron models with a sparsity prior
S Gerwinn, M Bethge, JH Macke, M Seeger
Advances in neural information processing systems 20, 2007
Statistical model checking for stochastic hybrid systems involving nondeterminism over continuous domains
C Ellen, S Gerwinn, M Fränzle
International Journal on Software Tools for Technology Transfer 17 (4), 485-504, 2015
Characterization of the p-generalized normal distribution
F Sinz, S Gerwinn, M Bethge
Journal of Multivariate Analysis 100 (5), 817-820, 2009
Gaussian process methods for estimating cortical maps
JH Macke, S Gerwinn, LE White, M Kaschube, M Bethge
neuroimage 56 (2), 570-581, 2011
Learning gaussian processes by minimizing pac-bayesian generalization bounds
D Reeb, A Doerr, S Gerwinn, B Rakitsch
Advances in Neural Information Processing Systems 31, 2018
Unsupervised learning of a steerable basis for invariant image representations
M Bethge, S Gerwinn, JH Macke
Human Vision and Electronic Imaging XII 6492, 132-143, 2007
Bayesian population decoding of spiking neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in computational neuroscience, 21, 2009
Support vector machines for an efficient representation of voltage band constraints
M Blank, S Gerwinn, O Krause, S Lehnhoff
2011 2nd IEEE PES International Conference and Exhibition on Innovative …, 2011
Formal synthesis and validation of inhomogeneous thermostatically controlled loads
S Esmaeil Zadeh Soudjani, S Gerwinn, C Ellen, M Fränzle, A Abate
International Conference on Quantitative Evaluation of Systems, 57-73, 2014
Reconstructing stimuli from the spike times of leaky integrate and fire neurons
S Gerwinn, JH Macke, M Bethge
Frontiers in neuroscience 5, 1, 2011
Efficient splitting of test and simulation cases for the verification of highly automated driving functions
E Böde, M Büker, U Eberle, M Fränzle, S Gerwinn, B Kramer
International Conference on Computer Safety, Reliability, and Security, 139-153, 2018
Design paradigms for multi-layer time coherency in ADAS and automated driving (MULTIC)
E Böde, M Büker, W Damm, G Ehmen, M Fränzle, S Gerwinn, ...
FAT Series, 2017
Neurometric function analysis of population codes
P Berens, S Gerwinn, A Ecker, M Bethge
Advances in neural information processing systems 22, 2009
Multi-objective parameter synthesis in probabilistic hybrid systems
M Fränzle, S Gerwinn, P Kröger, A Abate, JP Katoen
International conference on formal modeling and analysis of timed systems …, 2015
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