Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2513 | 2023 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 990 | 2024 |
A simulation-based architecture for smart cyber-physical systems T Gabor, L Belzner, M Kiermeier, MT Beck, A Neitz 2016 IEEE international conference on autonomic computing (ICAC), 374-379, 2016 | 380 | 2016 |
Neural Symbolic Regression that Scales L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo ICML 2021, 2021 | 192 | 2021 |
Learning explanations that are hard to vary G Parascandolo*, A Neitz*, A Orvieto, L Gresele, B Schölkopf ICLR 2021, 2021 | 192 | 2021 |
CausalWorld: A robotic manipulation benchmark for causal structure and transfer learning O Ahmed*, F Träuble*, A Goyal, A Neitz, Y Bengio, B Schölkopf, ... ICLR 2021, 2021 | 146 | 2021 |
Adaptive skip intervals: Temporal abstraction for recurrent dynamical models A Neitz, G Parascandolo, S Bauer, B Schölkopf NeurIPS 2018, 2018 | 43 | 2018 |
Divide-and-conquer monte carlo tree search for goal-directed planning G Parascandolo, L Buesing, J Merel, L Hasenclever, J Aslanides, ... arXiv preprint arXiv:2004.11410, 2020 | 34 | 2020 |
Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024 | 29 | 2024 |
Vision-language models as a source of rewards K Baumli, S Baveja, F Behbahani, H Chan, G Comanici, S Flennerhag, ... arXiv preprint arXiv:2312.09187, 2023 | 21 | 2023 |
Direct advantage estimation HR Pan, N Gürtler, A Neitz, B Schölkopf Advances in Neural Information Processing Systems 35, 11869-11880, 2022 | 15 | 2022 |
Predicting ordinary differential equations with transformers S Becker, M Klein, A Neitz, G Parascandolo, N Kilbertus International Conference on Machine Learning, 1978-2002, 2023 | 11 | 2023 |
Discovering ordinary differential equations that govern time-series S Becker, M Klein, A Neitz, G Parascandolo, N Kilbertus arXiv preprint arXiv:2211.02830, 2022 | 6 | 2022 |
Neural symbolic regression that scales (2021) L Biggio, T Bendinelli, A Neitz, A Lucchi, G Parascandolo arXiv preprint arXiv:2106.06427, 0 | 5 | |
A teacher-student framework to distill future trajectories A Neitz, G Parascandolo, B Schölkopf ICLR 2021, 0 | 5* | |
Risk-sensitivity in simulation based online planning K Schmid, L Belzner, M Kiermeier, A Neitz, T Phan, T Gabor, C Linnhoff KI 2018: Advances in Artificial Intelligence: 41st German Conference on AI …, 2018 | 1 | 2018 |
Learning relational probabilistic action models for online planning with decision forests L Belzner, A Neitz Proceedings of the 31st Annual ACM Symposium on Applied Computing, 248-253, 2016 | 1 | 2016 |
Towards learning mechanistic models at the right level of abstraction A Neitz Universität Tübingen, 2023 | | 2023 |
Efficient Discovery of Dynamical Laws in Symbolic Form S Becker, M Klein, A Neitz, G Parascandolo, N Kilbertus | | |