| Genetic attack on neural cryptography A Ruttor, W Kinzel, R Naeh, I Kanter Physical Review E 73 (3), 036121, 2006 | 121 | 2006 |
| Dynamics of neural cryptography A Ruttor, W Kinzel, I Kanter Physical Review E 75 (5), 056104, 2007 | 81 | 2007 |
| Neural synchronization and cryptography A Ruttor arXiv preprint arXiv:0711.2411, 2007 | 74 | 2007 |
| Synchronization of neural networks by mutual learning and its application to cryptography E Klein, R Mislovaty, I Kanter, A Ruttor, W Kinzel Advances in Neural Information Processing Systems 17, 2004 | 70 | 2004 |
| Approximate Gaussian process inference for the drift function in stochastic differential equations A Ruttor, P Batz, M Opper Advances in Neural Information Processing Systems 26, 2013 | 58 | 2013 |
| Neural cryptography with feedback A Ruttor, W Kinzel, L Shacham, I Kanter Physical Review E 69 (4), 046110, 2004 | 58 | 2004 |
| Neural cryptography with queries A Ruttor, W Kinzel, I Kanter Journal of Statistical Mechanics: Theory and Experiment 2005 (01), P01009, 2005 | 48 | 2005 |
| Approximate Bayes learning of stochastic differential equations P Batz, A Ruttor, M Opper Physical Review E 98 (2), 022109, 2018 | 43 | 2018 |
| Switching regulatory models of cellular stress response G Sanguinetti, A Ruttor, M Opper, C Archambeau Bioinformatics 25 (10), 1280-1286, 2009 | 42 | 2009 |
| Efficient statistical inference for stochastic reaction processes A Ruttor, M Opper Physical review letters 103 (23), 230601, 2009 | 35 | 2009 |
| Synchronization of random walks with reflecting boundaries A Ruttor, G Reents, W Kinzel Journal of Physics A: Mathematical and General 37 (36), 8609, 2004 | 25 | 2004 |
| Approximate inference in continuous time Gaussian-Jump processes M Opper, A Ruttor, G Sanguinetti Advances in Neural Information Processing Systems 23, 2010 | 24 | 2010 |
| Successful attack on permutation-parity-machine-based neural cryptography LF Seoane, A Ruttor Physical Review E 85 (2), 025101, 2012 | 20 | 2012 |
| Variational estimation of the drift for stochastic differential equations from the empirical density P Batz, A Ruttor, M Opper Journal of Statistical Mechanics: Theory and Experiment 2016 (8), 083404, 2016 | 16 | 2016 |
| Bayesian inference for change points in dynamical systems with reusable states-a chinese restaurant process approach F Stimberg, A Ruttor, M Opper Artificial Intelligence and Statistics, 1117-1124, 2012 | 16 | 2012 |
| Inference in continuous-time change-point models F Stimberg, M Opper, G Sanguinetti, A Ruttor Advances in Neural Information Processing Systems 24, 2011 | 16 | 2011 |
| Approximate Inference for Stochastic Reaction processes. A Ruttor, G Sanguinetti, M Opper, ND Lawrence, M Girolami, M Rattray Learning and Inference in Computational Systems Biology, 277-296, 2010 | 13 | 2010 |
| Approximate parameter inference in a stochastic reaction-diffusion model A Ruttor, M Opper Proceedings of the Thirteenth International Conference on Artificial …, 2010 | 10 | 2010 |
| Poisson process jumping between an unknown number of rates: application to neural spike data F Stimberg, A Ruttor, M Opper Advances in Neural Information Processing Systems 27, 2014 | 7 | 2014 |
| Advances in Neural Information Processing Systems 26 A Ruttor, P Batz, M Opper Curran Associates, Inc.) Go to reference in article, 2013 | 7 | 2013 |
