| BayesFlow: Learning complex stochastic models with invertible neural networks ST Radev, UK Mertens, A Voss, L Ardizzone, U Köthe IEEE Transactions on Neural Networks and Learning Systems 33 (4), 1452-1466, 2020 | 152 | 2020 |
| Measuring QCD splittings with invertible networks S Bieringer, A Butter, T Heimel, S Höche, U Köthe, T Plehn, ST Radev SciPost Physics 10 (6), 126, 2021 | 35 | 2021 |
| Towards end‐to‐end likelihood‐free inference with convolutional neural networks ST Radev, UK Mertens, A Voss, U Köthe British Journal of Mathematical and Statistical Psychology 73 (1), 23-43, 2020 | 33 | 2020 |
| Mental speed is high until age 60 as revealed by analysis of over a million participants M von Krause*, ST Radev*, A Voss Nature Human Behaviour 6 (5), 700-708, 2022 | 30 | 2022 |
| Amortized Bayesian model comparison with evidential deep learning ST Radev, M D'Alessandro, UK Mertens, A Voss, U Köthe, PC Bürkner IEEE Transactions on Neural Networks and Learning Systems 34 (8), 4903 - 4917, 2021 | 28 | 2021 |
| OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany ST Radev, F Graw, S Chen, NT Mutters, VM Eichel, T Bärnighausen, ... PLoS Computational Biology 17 (10), e1009472, 2021 | 26 | 2021 |
| Jumping to conclusion? A Lévy flight model of decision making EM Wieschen, A Voss, ST Radev The Quantitative Methods for Psychology 16 (2), 120-132, 2020 | 19 | 2020 |
| Detecting model misspecification in amortized Bayesian inference with neural networks M Schmitt, PC Bürkner, U Köthe, ST Radev arXiv preprint arXiv:2112.08866, 2021 | 18 | 2021 |
| Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback L Konicar, ST Radev, K Prillinger, M Klöbl, R Diehm, N Birbaumer, ... NeuroImage: Clinical 29, 102557, 2021 | 15 | 2021 |
| ABrox—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison UK Mertens, A Voss, ST Radev PLoS ONE 13 (3), e0193981, 2018 | 15 | 2018 |
| Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy PC Bürkner, M Scholz, ST Radev Statistics Surveys 17, 216-310, 2023 | 14 | 2023 |
| Repeated sessions of transcranial direct current stimulation on adolescents with autism spectrum disorder: study protocol for a randomized, double-blind, and sham-controlled … K Prillinger, ST Radev, G Amador de Lara, M Klöbl, R Lanzenberger, ... Frontiers in Psychiatry 12, 680525, 2021 | 13 | 2021 |
| JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models ST Radev, M Schmitt, V Pratz, U Picchini, U Köthe, PC Bürkner Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence …, 2023 | 12 | 2023 |
| Balancing the brain of offenders with psychopathy? Resting state EEG and electrodermal activity after a pilot study of brain self-regulation training L Konicar, ST Radev, S Silvoni, E Bolinger, R Veit, U Strehl, C Vesely, ... PLoS ONE 16 (1), e0242830, 2021 | 11 | 2021 |
| A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task M D’Alessandro*, ST Radev*, A Voss, L Lombardi PeerJ 8, e10316, 2020 | 11 | 2020 |
| BayesFlow: Amortized Bayesian workflows with neural networks ST Radev, M Schmitt, L Schumacher, L Elsemüller, V Pratz, Y Schälte, ... Journal of Open Source Software 8 (89), 5702, 2023 | 9 | 2023 |
| Meta-Uncertainty in Bayesian Model Comparison M Schmitt, ST Radev, PC Bürkner Proceedings of the 26th International Conference on Artificial Intelligence …, 2023 | 8 | 2023 |
| Evaluation of neurofeedback learning in patients with ADHD: A systematic review E Kuznetsova, AVP Veilahti, R Akhundzadeh, S Radev, L Konicar, ... Applied Psychophysiology and Biofeedback 48 (1), 11-25, 2023 | 8 | 2023 |
| Gamma band oscillations reflect sensory and affective dimensions of pain Y Lyu, F Zidda, ST Radev, H Liu, X Guo, S Tong, H Flor, J Andoh Frontiers in Neurology 12, 695187, 2022 | 7 | 2022 |
| A deep learning method for comparing bayesian hierarchical models L Elsemüller, M Schnuerch, PC Bürkner, ST Radev arXiv preprint arXiv:2301.11873, 2023 | 6 | 2023 |
Ullrich KötheAdjunct Professor of Computer Science, University of HeidelbergVerified email at iwr.uni-heidelberg.de
