| 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 | 1103 | 2014 |
| 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 | 289 | 2020 |
| Shark. C Igel, V Heidrich-Meisner, T Glasmachers Journal of machine learning research 9 (6), 2008 | 275 | 2008 |
| Limits of end-to-end learning T Glasmachers Asian conference on machine learning, 17-32, 2017 | 245 | 2017 |
| 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 | 236 | 2010 |
| 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 | 156 | 2011 |
| A unified view on multi-class support vector classification T Glasmachers, C Igel Journal of Machine Learning Research 17 (45), 1-32, 2016 | 135 | 2016 |
| Maximum-gain working set selection for SVMs T Glasmachers, C Igel The Journal of Machine Learning Research 7, 1437-1466, 2006 | 116 | 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 | 74 | 2007 |
| Gradient-based adaptation of general Gaussian kernels T Glasmachers, C Igel Neural Computation 17 (10), 2099-2105, 2005 | 73 | 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 | 72 | 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 | 69 | 2019 |
| 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 | 54 | 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 | 47 | 2010 |
| Accelerated coordinate descent with adaptive coordinate frequencies T Glasmachers, U Dogan Asian Conference on Machine Learning, 72-86, 2013 | 40 | 2013 |
| A natural evolution strategy for multi-objective optimization T Glasmachers, T Schaul, J Schmidhuber International Conference on Parallel Problem Solving from Nature, 627-636, 2010 | 40 | 2010 |
| Second-order SMO improves SVM online and active learning T Glasmachers, C Igel Neural Computation 20 (2), 374-382, 2008 | 39 | 2008 |
| SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm M Saif-ur-Rehman, O Ali, S Dyck, R Lienkämper, M Metzler, Y Parpaley, ... Journal of Neural Engineering 18 (1), 016009, 2021 | 38 | 2021 |
| Drift theory in continuous search spaces: expected hitting time of the (1+ 1)-ES with 1/5 success rule Y Akimoto, A Auger, T Glasmachers Proceedings of the Genetic and Evolutionary Computation Conference, 801-808, 2018 | 37 | 2018 |
| Challenges in high-dimensional reinforcement learning with evolution strategies N Müller, T Glasmachers Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018 | 37 | 2018 |
