| On detecting adversarial perturbations JH Metzen, T Genewein, V Fischer, B Bischoff ICLR 2017, arXiv preprint arXiv:1702.04267, 2017 | 1175 | 2017 |
| The power of ensembles for active learning in image classification WH Beluch, T Genewein, A Nürnberger, JM Köhler Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 822 | 2018 |
| Variational Network Quantization J Achterhold, JM Koehler, A Schmeink, T Genewein ICLR 2018, International Conference on Learning Representations, 1-18, 2018 | 131 | 2018 |
| Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle T Genewein, F Leibfried, J Grau-Moya, DA Braun Frontiers in Robotics and AI 2, 27, 2015 | 119 | 2015 |
| Sinkhorn autoencoders G Patrini, R Van den Berg, P Forre, M Carioni, S Bhargav, M Welling, ... Uncertainty in Artificial Intelligence, 733-743, 2020 | 115 | 2020 |
| Neural networks and the chomsky hierarchy G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... arXiv preprint arXiv:2207.02098, 2022 | 108 | 2022 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 96 | 2019 |
| Language modeling is compression G Delétang, A Ruoss, PA Duquenne, E Catt, T Genewein, C Mattern, ... arXiv preprint arXiv:2309.10668, 2023 | 89 | 2023 |
| Randomized positional encodings boost length generalization of transformers A Ruoss, G Delétang, T Genewein, J Grau-Moya, R Csordás, M Bennani, ... arXiv preprint arXiv:2305.16843, 2023 | 57 | 2023 |
| Probabilistic binary neural networks JWT Peters, M Welling arXiv preprint arXiv:1809.03368, 2018 | 56 | 2018 |
| Shaking the foundations: delusions in sequence models for interaction and control PA Ortega, M Kunesch, G Delétang, T Genewein, J Grau-Moya, J Veness, ... arXiv preprint arXiv:2110.10819, 2021 | 55 | 2021 |
| Meta-trained agents implement bayes-optimal agents V Mikulik, G Delétang, T McGrath, T Genewein, M Martic, S Legg, ... Advances in neural information processing systems 33, 18691-18703, 2020 | 41 | 2020 |
| Planning with information-processing constraints and model uncertainty in Markov decision processes J Grau-Moya, F Leibfried, T Genewein, DA Braun ECML 2016, Joint European Conference on Machine Learning and Knowledge …, 2016 | 37 | 2016 |
| Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences Z Peng, T Genewein, DA Braun Frontiers in human neuroscience 8, 168, 2014 | 24 | 2014 |
| Algorithms for causal reasoning in probability trees T Genewein, T McGrath, G Delétang, V Mikulik, M Martic, S Legg, ... arXiv preprint arXiv:2010.12237, 2020 | 20 | 2020 |
| Structure learning in Bayesian sensorimotor integration T Genewein, E Hez, Z Razzaghpanah, DA Braun PLoS computational biology 11 (8), e1004369, 2015 | 19 | 2015 |
| An information-theoretic on-line update principle for perception-action coupling Z Peng, T Genewein, F Leibfried, DA Braun 2017 IEEE/RSJ International Conference on intelligent robots and systems …, 2017 | 18 | 2017 |
| Grandmaster-level chess without search A Ruoss, G Delétang, S Medapati, J Grau-Moya, LK Wenliang, E Catt, ... arXiv preprint arXiv:2402.04494, 2024 | 17 | 2024 |
| Occam's Razor in sensorimotor learning T Genewein, DA Braun Proceedings of the Royal Society B: Biological Sciences 281 (1783), 20132952, 2014 | 17 | 2014 |
| A sensorimotor paradigm for Bayesian model selection T Genewein, DA Braun Frontiers in human neuroscience 6, 291, 2012 | 14 | 2012 |
Jordi Grau-MoyaResearch Scientist at Google DeepMindVerified email at deepmind.com
