Tobias Glasmachers
Tobias Glasmachers
Afiliere necunoscută
Adresă de e-mail confirmată pe idsia.ch
Citat de
Citat de
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
C Igel, V Heidrich-Meisner, T Glasmachers
Journal of machine learning research 9 (6), 2008
AI for social good: unlocking the opportunity for positive impact
N Tomaąev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ...
Nature Communications 11 (1), 2468, 2020
Limits of end-to-end learning
T Glasmachers
Asian conference on machine learning, 17-32, 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
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
A unified view on multi-class support vector classification
T Glasmachers, C Igel
Journal of Machine Learning Research 17 (45), 1-32, 2016
Maximum-gain working set selection for SVMs
T Glasmachers, C Igel
The Journal of Machine Learning Research 7, 1437-1466, 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
Gradient-based adaptation of general Gaussian kernels
T Glasmachers, C Igel
Neural Computation 17 (10), 2099-2105, 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
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
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
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
Accelerated coordinate descent with adaptive coordinate frequencies
T Glasmachers, U Dogan
Asian Conference on Machine Learning, 72-86, 2013
Second-order SMO improves SVM online and active learning
T Glasmachers, C Igel
Neural Computation 20 (2), 374-382, 2008
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
Challenges in high-dimensional reinforcement learning with evolution strategies
N Müller, T Glasmachers
Parallel Problem Solving from Nature–PPSN XV: 15th International Conference …, 2018
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
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
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