| Learning to control redundant musculoskeletal systems with neural networks and SQP: exploiting muscle properties D Driess, H Zimmermann, S Wolfen, D Suissa, D Haeufle, D Hennes, ... 2018 IEEE International Conference on Robotics and Automation (ICRA), 6461-6468, 2018 | 15 | 2018 |
| Bayesian functional optimization NA Vien, H Zimmermann, M Toussaint Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 11 | 2018 |
| Optimality principles in human point-to-manifold reaching accounting for muscle dynamics I Wochner, D Driess, H Zimmermann, DFB Haeufle, M Toussaint, ... Frontiers in computational neuroscience 14, 38, 2020 | 9 | 2020 |
| Learning Proposals for Probabilistic Programs with Inference Combinators S Stites, H Zimmermann, H Wu, E Sennesh, JW van de Meent arXiv preprint arXiv:2103.00668, 2021 | 4 | 2021 |
| Amortized population gibbs samplers with neural sufficient statistics H Wu, H Zimmermann, E Sennesh, TA Le, JW Van De Meent International Conference on Machine Learning 119, 10421--10431, 2020 | 4 | 2020 |
| Nested Variational Inference H Zimmermann, H Wu, B Esmaeili, JW van de Meent Advances in Neural Information Processing Systems 34, 2021 | 3 | 2021 |
| Bayesian Functional Optimization Download PDF NA Vien, H Zimmermann, M Toussaint | | |
| Supplementary Material: Learning Proposals for Probabilistic Programs with Inference Combinators S Stites, H Zimmermann, H Wu, E Sennesh, JW van de Meent | | |
| Supplementary Material: Optimality principles in human point-to-manifold reaching accounting for muscle dynamics I Wochner, D Driess, H Zimmermann, DFB Haeufle, M Toussaint, ... | | |
| Probabilistic Program Inference in Network-based Epidemiological Simulations N Smedemark-Margulies, R Walters, H Zimmermann, L Laird, ... | | |
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
