| Exploration in model-based reinforcement learning by empirically estimating learning progress M Lopes, T Lang, M Toussaint, PY Oudeyer Advances in neural information processing systems 25, 2012 | 241 | 2012 |
| Adaptive Non-Stationary Kernel Regression for Terrain Modeling. T Lang, C Plagemann, W Burgard Robotics: Science and Systems 6, 2007 | 111 | 2007 |
| Active Learning for Teaching a Robot Grounded Relational Symbols. J Kulick, M Toussaint, T Lang, M Lopes IJCAI, 1451-1457, 2013 | 101 | 2013 |
| Planning with noisy probabilistic relational rules T Lang, M Toussaint Journal of Artificial Intelligence Research 39, 1-49, 2010 | 91 | 2010 |
| Exploration in relational domains for model-based reinforcement learning T Lang, M Toussaint, K Kersting The Journal of Machine Learning Research 13 (1), 3725-3768, 2012 | 75 | 2012 |
| Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference M Toussaint, N Plath, T Lang, N Jetchev 2010 IEEE International Conference on Robotics and Automation, 385-391, 2010 | 72 | 2010 |
| Understanding consumer behavior with recurrent neural networks T Lang, M Rettenmeier Workshop on Machine Learning Methods for Recommender Systems, 2017 | 64 | 2017 |
| Feasibility of active machine learning for multiclass compound classification T Lang, F Flachsenberg, U von Luxburg, M Rarey Journal of chemical information and modeling 56 (1), 12-20, 2016 | 48 | 2016 |
| Learning grounded relational symbols from continuous data for abstract reasoning N Jetchev, T Lang, M Toussaint Proceedings of the 2013 ICRA Workshop on Autonomous Learning, 2013 | 45 | 2013 |
| Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology A Homeyer, C Geißler, LO Schwen, F Zakrzewski, T Evans, ... Modern Pathology 35 (12), 1759-1769, 2022 | 34 | 2022 |
| Relevance grounding for planning in relational domains T Lang, M Toussaint Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009 | 28 | 2009 |
| Approximate inference for planning in stochastic relational worlds T Lang, M Toussaint Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 25 | 2009 |
| A deep learning approach for histopathological diagnosis of onychomycosis: not inferior to analogue diagnosis by histopathologists F Decroos, S Springenberg, L Tobias, M Paepper, Z Antonia, D Metze, ... Acta Dermato-Venereologica 101 (8), 2021 | 24 | 2021 |
| Exploration in relational worlds T Lang, M Toussaint, K Kersting Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 19 | 2010 |
| Extracting kinematic background knowledge from interactions using task-sensitive relational learning S Höfer, T Lang, O Brock 2014 IEEE International Conference on Robotics and Automation (ICRA), 4342-4347, 2014 | 13 | 2014 |
| Analyzing and escaping local optima in planning as inference for partially observable domains P Poupart, T Lang, M Toussaint Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 12 | 2011 |
| Noninferiority of Artificial Intelligence–Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics N Abele, K Tiemann, T Krech, A Wellmann, C Schaaf, F Länger, A Peters, ... Modern Pathology 36 (3), 100033, 2023 | 11 | 2023 |
| Planning and exploration in stochastic relational worlds TJ Lang | 11 | 2011 |
| Probabilistic backward and forward reasoning in stochastic relational worlds T Lang, M Toussaint Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 6 | 2010 |
| Kognitive Robotik—Herausforderungen an unser Verständnis natürlicher Umgebungen M Toussaint, T Lang, N Jetchev Oldenbourg Wissenschaftsverlag GmbH 61 (4), 259-268, 2013 | 5 | 2013 |
