| Natural evolution strategies D Wierstra, T Schaul, T Glasmachers, Y Sun, J Peters, J Schmidhuber The Journal of Machine Learning Research 15 (1), 949-980, 2014 | 833 | 2014 |
| Shark. C Igel, V Heidrich-Meisner, T Glasmachers Journal of machine learning research 9 (6), 2008 | 259 | 2008 |
| Exponential natural evolution strategies T Glasmachers, T Schaul, S Yi, D Wierstra, J Schmidhuber Proceedings of the 12th annual conference on Genetic and evolutionary …, 2010 | 194 | 2010 |
| Limits of end-to-end learning T Glasmachers Asian conference on machine learning, 17-32, 2017 | 169 | 2017 |
| AI for social good: unlocking the opportunity for positive impact N Tomašev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ... Nature Communications 11 (1), 2468, 2020 | 121 | 2020 |
| A Unified View on Multi-class Support Vector Classification. Ü Dogan, T Glasmachers, C Igel J. Mach. Learn. Res. 17 (45), 1-32, 2016 | 106 | 2016 |
| High dimensions and heavy tails for natural evolution strategies T Schaul, T Glasmachers, J Schmidhuber Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011 | 106 | 2011 |
| Maximum-gain working set selection for SVMs T Glasmachers, C Igel The Journal of Machine Learning Research 7, 1437-1466, 2006 | 106 | 2006 |
| Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection C Igel, T Glasmachers, B Mersch, N Pfeifer, P Meinicke IEEE/ACM Transactions on Computational Biology and Bioinformatics 4 (2), 216-226, 2007 | 73 | 2007 |
| Gradient-based adaptation of general Gaussian kernels T Glasmachers, C Igel Neural Computation 17 (10), 2099-2105, 2005 | 71 | 2005 |
| Large scale black-box optimization by limited-memory matrix adaptation I Loshchilov, T Glasmachers, HG Beyer IEEE Transactions on Evolutionary Computation 23 (2), 353-358, 2018 | 49 | 2018 |
| Modeling macroscopic material behavior with machine learning algorithms trained by micromechanical simulations D Reimann, K Nidadavolu, H ul Hassan, N Vajragupta, T Glasmachers, ... Frontiers in Materials 6, 181, 2019 | 46 | 2019 |
| Maximum likelihood model selection for 1-norm soft margin SVMs with multiple parameters T Glasmachers, C Igel IEEE transactions on pattern analysis and machine intelligence 32 (8), 1522-1528, 2010 | 45 | 2010 |
| Accelerated coordinate descent with adaptive coordinate frequencies T Glasmachers, U Dogan Asian Conference on Machine Learning, 72-86, 2013 | 40 | 2013 |
| Second-order SMO improves SVM online and active learning T Glasmachers, C Igel Neural Computation 20 (2), 374-382, 2008 | 39 | 2008 |
| SpikeDeeptector: A deep-learning based method for detection of neural spiking activity M Saif-ur-Rehman, R Lienkämper, Y Parpaley, J Wellmer, C Liu, B Lee, ... Journal of neural engineering 16 (5), 056003, 2019 | 38 | 2019 |
| A natural evolution strategy for multi-objective optimization T Glasmachers, T Schaul, J Schmidhuber Parallel Problem Solving from Nature, PPSN XI: 11th International Conference …, 2010 | 35 | 2010 |
| Evolutionary optimization of sequence kernels for detection of bacterial gene starts B Mersch, T Glasmachers, P Meinicke, C Igel International Journal of Neural Systems 17 (5), 369-381, 2007 | 33 | 2007 |
| Natural evolution strategies D Wierstra, T Schaul, T Glasmachers, Y Sun, J Schmidhuber arXiv preprint arXiv:1106.4487, 2011 | 29 | 2011 |
| Artificial curiosity for autonomous space exploration V Graziano, T Glasmachers, T Schaul, L Pape, G Cuccu, J Leitner, ... Acta Futura 4, 41-51, 2011 | 28 | 2011 |
