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ORCIDStefan T. Radev
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2020 – today
- 2024
[c4]Marvin Schmitt, Desi R. Ivanova, Daniel Habermann, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev:
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. ICML 2024- [i18]Marvin Schmitt, Yuga Hikida, Stefan T. Radev, Filip Sadlo, Paul-Christian Bürkner:
The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas. CoRR abs/2403.11141 (2024) - [i17]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation. CoRR abs/2406.03154 (2024) - [i16]Daniel Habermann, Marvin Schmitt, Lars Kühmichel, Andreas Bulling, Stefan T. Radev, Paul-Christian Bürkner:
Amortized Bayesian Multilevel Models. CoRR abs/2408.13230 (2024) - 2023
- [j5]Stefan T. Radev
, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner:
BayesFlow: Amortized Bayesian Workflows With Neural Networks. J. Open Source Softw. 8(90): 5702 (2023) - [j4]Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe:
Finding Competence Regions in Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner:
Amortized Bayesian Model Comparison With Evidential Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34(8): 4903-4917 (2023) - [c3]Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner:
Meta-Uncertainty in Bayesian Model Comparison. AISTATS 2023: 11-29 - [c2]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks. DAGM 2023: 541-557 - [c1]Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
Jana: Jointly amortized neural approximation of complex Bayesian models. UAI 2023: 1695-1706 - [i15]Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T. Radev:
A Deep Learning Method for Comparing Bayesian Hierarchical Models. CoRR abs/2301.11873 (2023) - [i14]Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner:
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. CoRR abs/2302.09125 (2023) - [i13]Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe:
Finding Competence Regions in Domain Generalization. CoRR abs/2303.09989 (2023) - [i12]Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner:
BayesFlow: Amortized Bayesian Workflows With Neural Networks. CoRR abs/2306.16015 (2023) - [i11]Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. CoRR abs/2310.04395 (2023) - [i10]Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
Sensitivity-Aware Amortized Bayesian Inference. CoRR abs/2310.11122 (2023) - [i9]Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner:
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference. CoRR abs/2311.10671 (2023) - [i8]Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev:
Consistency Models for Scalable and Fast Simulation-Based Inference. CoRR abs/2312.05440 (2023) - [i7]Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe:
Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks. CoRR abs/2312.10107 (2023) - 2022
- [j2]Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe:
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33(4): 1452-1466 (2022) - [i6]Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner:
Meta-Uncertainty in Bayesian Model Comparison. CoRR abs/2210.07278 (2022) - 2021
- [b1]Stefan T. Radev:
Deep Learning Architectures for Amortized Bayesian Inference in Cognitive Modeling. University of Heidelberg, Germany, 2021 - [j1]Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe:
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany. PLoS Comput. Biol. 17(10) (2021) - [i5]Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev:
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. CoRR abs/2112.08866 (2021) - 2020
- [i4]Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe:
BayesFlow: Learning complex stochastic models with invertible neural networks. CoRR abs/2003.06281 (2020) - [i3]Stefan T. Radev, Marco D'Alessandro, Paul-Christian Bürkner, Ulf K. Mertens, Andreas Voss, Ullrich Köthe:
Amortized Bayesian model comparison with evidential deep learning. CoRR abs/2004.10629 (2020) - [i2]Stefan T. Radev, Andreas Voss, Eva Marie Wieschen, Paul-Christian Bürkner:
Amortized Bayesian Inference for Models of Cognition. CoRR abs/2005.03899 (2020) - [i1]Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe:
Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks. CoRR abs/2010.00300 (2020)
Coauthor Index
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last updated on 2024-09-30 01:01 CEST by the dblp team
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