| Striving for simplicity: The all convolutional net JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller arXiv preprint arXiv:1412.6806, 2014 | 4865 | 2014 |
| Efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in neural information processing systems 28, 2015 | 2167 | 2015 |
| 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 | 1852 | 2017 |
| Discriminative unsupervised feature learning with convolutional neural networks A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox Advances in neural information processing systems 27, 2014 | 1649 | 2014 |
| Unsupervised and semi-supervised learning with categorical generative adversarial networks JT Springenberg arXiv preprint arXiv:1511.06390, 2015 | 848 | 2015 |
| 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 | 772 | 2015 |
| 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 28, 2015 | 765 | 2015 |
| 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 | 717 | 2015 |
| 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 | 647 | 2015 |
| Graph networks as learnable physics engines for inference and control A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ... International Conference on Machine Learning, 4470-4479, 2018 | 554 | 2018 |
| Initializing bayesian hyperparameter optimization via meta-learning M Feurer, J Springenberg, F Hutter Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 461 | 2015 |
| Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 2016 | 416 | 2016 |
| Learning by playing solving sparse reward tasks from scratch M Riedmiller, R Hafner, T Lampe, M Neunert, J Degrave, T Wiele, V Mnih, ... International conference on machine learning, 4344-4353, 2018 | 382 | 2018 |
| Maximum a posteriori policy optimisation A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ... arXiv preprint arXiv:1806.06920, 2018 | 377 | 2018 |
| Learning an embedding space for transferable robot skills K Hausman, JT Springenberg, Z Wang, N Heess, M Riedmiller International Conference on Learning Representations, 2018 | 276 | 2018 |
| A generalist agent S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ... arXiv preprint arXiv:2205.06175, 2022 | 266 | 2022 |
| 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 | 255 | 2012 |
| 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 | 253 | 2017 |
| 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 | 236 | 2016 |
| Towards automatically-tuned neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter Workshop on automatic machine learning, 58-65, 2016 | 235 | 2016 |
