| Large scale transductive SVMs. R Collobert, F Sinz, J Weston, L Bottou, T Joachims Journal of Machine Learning Research 7 (8), 2006 | 532 | 2006 |
| Principles of connectivity among morphologically defined cell types in adult neocortex X Jiang, S Shen, CR Cadwell, P Berens, F Sinz, AS Ecker, S Patel, ... Science 350 (6264), 2015 | 522 | 2015 |
| Trading convexity for scalability R Collobert, F Sinz, J Weston, L Bottou Proceedings of the 23rd international conference on Machine learning, 201-208, 2006 | 376 | 2006 |
| Inference with the universum J Weston, R Collobert, F Sinz, L Bottou, V Vapnik Proceedings of the 23rd international conference on Machine learning, 1009-1016, 2006 | 174 | 2006 |
| Population code in mouse V1 facilitates readout of natural scenes through increased sparseness E Froudarakis, P Berens, AS Ecker, RJ Cotton, FH Sinz, D Yatsenko, ... Nature neuroscience 17 (6), 851-857, 2014 | 133 | 2014 |
| An analysis of inference with the universum FH Sinz, O Chapelle, A Agarwal, B Schölkopf Advances in Neural Information Processing Systems 20: 21st Annual Conference …, 2008 | 88 | 2008 |
| Evaluating predictive uncertainty challenge J Quinonero-Candela, CE Rasmussen, F Sinz, O Bousquet, B Schölkopf Machine Learning Challenges Workshop, 1-27, 2005 | 79 | 2005 |
| Normalizing the normalizers: Comparing and extending network normalization schemes M Ren, R Liao, R Urtasun, FH Sinz, RS Zemel arXiv preprint arXiv:1611.04520, 2016 | 68 | 2016 |
| Learning depth from stereo F Sinz, J Candela, G Bakır, C Rasmussen, M Franz 26th DAGM Symposium, 245-252, 2004 | 61 | 2004 |
| Natural image coding in V1: how much use is orientation selectivity? J Eichhorn, F Sinz, M Bethge PLoS Comput Biol 5 (4), e1000336, 2009 | 60 | 2009 |
| Engineering a less artificial intelligence FH Sinz, X Pitkow, J Reimer, M Bethge, AS Tolias Neuron 103 (6), 967-979, 2019 | 50 | 2019 |
| Alternative splicing substantially diversifies the transcriptome during early photomorphogenesis and correlates with the energy availability in Arabidopsis L Hartmann, P Drewe-Boß, T Wießner, G Wagner, S Geue, HC Lee, ... The Plant Cell 28 (11), 2715-2734, 2016 | 50 | 2016 |
| 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 | 45 | 2011 |
| The spider machine learning toolbox J Weston, A Elisseeff, G Bakir, F Sinz Software available at http://www. kyb. tuebingen. mpg. de/bs/people/spider, 2005 | 34 | 2005 |
| The conjoint effect of divisive normalization and orientation selectivity on redundancy reduction F Sinz, M Bethge 22nd Annual Conference on Neural Information Processing Systems 2008, 8, 2009 | 33* | 2009 |
| The spider J Weston, A Elisseeff, G Bakir, F Sinz Machine Learning Toolbox http://www. kyb. tuebingen. mpg. de/bs/people/spider, 2006 | 33 | 2006 |
| Characterization of the p-generalized normal distribution F Sinz, S Gerwinn, M Bethge Journal of Multivariate Analysis 100 (5), 817-820, 2009 | 31 | 2009 |
| Temporal adaptation enhances efficient contrast gain control on natural images F Sinz, M Bethge PLoS Comput Biol 9 (1), e1002889, 2013 | 30 | 2013 |
| DataJoint: managing big scientific data using MATLAB or Python D Yatsenko, J Reimer, AS Ecker, EY Walker, F Sinz, P Berens, ... BioRxiv, 031658, 2015 | 27 | 2015 |
| Inception loops discover what excites neurons most using deep predictive models EY Walker, FH Sinz, E Cobos, T Muhammad, E Froudarakis, PG Fahey, ... Nature neuroscience 22 (12), 2060-2065, 2019 | 26 | 2019 |
Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
