| Quantum chemical accuracy from density functional approximations via machine learning M Bogojeski, L Vogt-Maranto, ME Tuckerman, KR Müller, K Burke Nature communications 11 (1), 1-11, 2020 | 328 | 2020 |
| SE (3)-equivariant prediction of molecular wavefunctions and electronic densities O Unke*, M Bogojeski*, M Gastegger, M Geiger, T Smidt, KR Müller Advances in Neural Information Processing Systems 34, 14434-14447, 2021 | 105 | 2021 |
| Real-time inference of word relevance from electroencephalogram and eye gaze MA Wenzel, M Bogojeski, B Blankertz Journal of neural engineering 14 (5), 056007, 2017 | 31 | 2017 |
| Forecasting industrial aging processes with machine learning methods M Bogojeski, S Sauer, F Horn, KR Müller Computers & Chemical Engineering 144, 107123, 2021 | 29 | 2021 |
| Implicit relevance feedback from electroencephalography and eye tracking in image search JE Golenia, MA Wenzel, M Bogojeski, B Blankertz Journal of neural engineering 15 (2), 026002, 2018 | 20 | 2018 |
| Efficient prediction of 3D electron densities using machine learning M Bogojeski, F Brockherde, L Vogt-Maranto, L Li, ME Tuckerman, K Burke, ... arXiv preprint arXiv:1811.06255, 2018 | 19 | 2018 |
| Density functionals with quantum chemical accuracy: from machine learning to molecular dynamics M Bogojeski, L Vogt-Maranto, ME Tuckerman, KR Mueller, K Burke preprint at 10, 2019 | 12 | 2019 |
| Learning more expressive joint distributions in multimodal variational methods S Nedelkoski, M Bogojeski, O Kao Machine Learning, Optimization, and Data Science: 6th International …, 2020 | 7 | 2020 |
| On sequent-type rejection calculi for many-valued logics M Bogojeski, H Tompits Reasoning: Games, Cognition, Logic; Urbanski, M., Skura, T., Łupkowski, P …, 2020 | 6 | 2020 |
| Neural responses to abstract and linguistic stimuli with variable recognition latency MA Wenzel, C Moreira, IA Lungu, M Bogojeski, B Blankertz Symbiotic Interaction: 4th International Workshop, Symbiotic 2015, Berlin …, 2015 | 4 | 2015 |
| Augmentation of multimodal time series data for training machine-learning models N Yakut, M Bogojeski, K Mueller US Patent App. 17/793,728, 2023 | 1 | 2023 |
| A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery P Semnani, M Bogojeski, F Bley, Z Zhang, Q Wu, T Kneib, J Herrmann, ... The Journal of Physical Chemistry C 128 (50), 21349-21367, 2024 | | 2024 |
| Exploring equivariant models for electronic properties KR Muller, M Bogojeski Bulletin of the American Physical Society, 2024 | | 2024 |
| Forecasting industrial aging processes with machine learning methods N Yakut, S Sauer, M Bogojeski, F Horn, K Mueller US Patent 11,860,617, 2024 | | 2024 |
| Machine learning for electronic structure M Bogojeski PQDT-Global, 2023 | | 2023 |
| Data Augmentation for Forecasting Industrial Aging Processes Via Conditional Multimodal Generative Time-Series Models M Bogojeski, N Yakut, S Nedelkoski, S Nakajima, KR Müller Available at SSRN 5077317, 0 | | |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de