Jost Tobias Springenberg
Jost Tobias Springenberg
Verified email at informatik.uni-freiburg.de - Homepage
TitleCited byYear
Striving for simplicity: The all convolutional net
JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller
arXiv preprint arXiv:1412.6806, 2014
17272014
Efficient and robust automated machine learning
M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter
Advances in neural information processing systems, 2962-2970, 2015
5392015
Learning to generate chairs with convolutional neural networks
A Dosovitskiy, J Tobias Springenberg, T Brox
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
5282015
Multimodal deep learning for robust RGB-D object recognition
A Eitel, JT Springenberg, L Spinello, M Riedmiller, W Burgard
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
3802015
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ...
Human brain mapping 38 (11), 5391-5420, 2017
379*2017
Unsupervised and semi-supervised learning with categorical generative adversarial networks
JT Springenberg
arXiv preprint arXiv:1511.06390, 2015
3722015
Discriminative unsupervised feature learning with convolutional neural networks
A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox
Advances in neural information processing systems, 766-774, 2014
3142014
Embed to control: A locally linear latent dynamics model for control from raw images
M Watter, J Springenberg, J Boedecker, M Riedmiller
Advances in neural information processing systems, 2746-2754, 2015
3072015
A learned feature descriptor for object recognition in rgb-d data
M Blum, JT Springenberg, J Wülfing, M Riedmiller
2012 IEEE International Conference on Robotics and Automation, 1298-1303, 2012
2102012
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T Domhan, JT Springenberg, F Hutter
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
2062015
Initializing bayesian hyperparameter optimization via meta-learning
M Feurer, JT Springenberg, F Hutter
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
1792015
Discriminative unsupervised feature learning with exemplar convolutional neural networks
A Dosovitskiy, P Fischer, JT Springenberg, M Riedmiller, T Brox
IEEE transactions on pattern analysis and machine intelligence 38 (9), 1734-1747, 2015
1482015
Learning to generate chairs, tables and cars with convolutional networks
A Dosovitskiy, JT Springenberg, M Tatarchenko, T Brox
IEEE transactions on pattern analysis and machine intelligence 39 (4), 692-705, 2016
1232016
Learning by playing-solving sparse reward tasks from scratch
M Riedmiller, R Hafner, T Lampe, M Neunert, J Degrave, T Van de Wiele, ...
arXiv preprint arXiv:1802.10567, 2018
922018
Deep reinforcement learning with successor features for navigation across similar environments
J Zhang, JT Springenberg, J Boedecker, W Burgard
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017
912017
Graph networks as learnable physics engines for inference and control
A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ...
arXiv preprint arXiv:1806.01242, 2018
902018
Improving deep neural networks with probabilistic maxout units
JT Springenberg, M Riedmiller
arXiv preprint arXiv:1312.6116, 2013
792013
Towards automatically-tuned neural networks
H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter
Workshop on Automatic Machine Learning, 58-65, 2016
752016
Maximum a posteriori policy optimisation
A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ...
arXiv preprint arXiv:1806.06920, 2018
652018
Learning an embedding space for transferable robot skills
K Hausman, JT Springenberg, Z Wang, N Heess, M Riedmiller
642018
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