| 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 | 182 | 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 | 107 | 2013 |
| Bayesian inference for generalized linear models for spiking neurons S Gerwinn, JH Macke, M Bethge Frontiers in computational neuroscience 4, 1299, 2010 | 89 | 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 | 72 | 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 | 45 | 2018 |
| 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, 485-504, 2015 | 45 | 2015 |
| 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 | 42 | 2011 |
| Bayesian inference for sparse generalized linear models M Seeger, S Gerwinn, M Bethge European Conference on Machine Learning, 298-309, 2007 | 42 | 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 | 40 | 2007 |
| Characterization of the p-generalized normal distribution F Sinz, S Gerwinn, M Bethge Journal of Multivariate Analysis 100 (5), 817-820, 2009 | 37 | 2009 |
| Gaussian process methods for estimating cortical maps JH Macke, S Gerwinn, LE White, M Kaschube, M Bethge NeuroImage 56 (2), 570-581, 2011 | 35 | 2011 |
| 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 | 32 | 2007 |
| Learning partially known stochastic dynamics with empirical PAC Bayes M Haußmann, S Gerwinn, A Look, B Rakitsch, M Kandemir International conference on artificial intelligence and statistics, 478-486, 2021 | 22 | 2021 |
| Bayesian population decoding of spiking neurons S Gerwinn, JH Macke, M Bethge Frontiers in computational neuroscience 3, 648, 2009 | 22 | 2009 |
| 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 Computer Safety, Reliability, and Security: 37th International Conference …, 2018 | 21 | 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 Forschungsvereinigung Automobiltechnik, 2017 | 18 | 2017 |
| 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 | 18 | 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 | 17 | 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 | 17 | 2011 |
| Bayesian evidential deep learning with PAC regularization M Haussmann, S Gerwinn, M Kandemir arXiv preprint arXiv:1906.00816, 2019 | 13 | 2019 |
Matthias BethgeTübingen University & Maddox Co-FounderPatvirtintas el. paštas bethgelab.org
