Stefan T. Radev

According to our database, Stefan T. Radev authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas.

Marvin Schmitt, Yuga Hikida, Stefan T. Radev, Filip Sadlo, Paul-Christian Bürkner

CoRR, 2024

2023

BayesFlow: Amortized Bayesian Workflows With Neural Networks.

Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner

J. Open Source Softw., October, 2023

Amortized Bayesian Model Comparison With Evidential Deep Learning.

Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner

IEEE Trans. Neural Networks Learn. Syst., August, 2023

Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks.

Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe

CoRR, 2023

Consistency Models for Scalable and Fast Simulation-Based Inference.

Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev

CoRR, 2023

Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference.

Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

CoRR, 2023

Sensitivity-Aware Amortized Bayesian Inference.

Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

CoRR, 2023

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference.

Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

CoRR, 2023

Finding Competence Regions in Domain Generalization.

Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe

CoRR, 2023

A Deep Learning Method for Comparing Bayesian Hierarchical Models.

Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T. Radev

CoRR, 2023

Jana: Jointly amortized neural approximation of complex Bayesian models.

Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks.

Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

Meta-Uncertainty in Bayesian Model Comparison.

Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe

IEEE Trans. Neural Networks Learn. Syst., 2022

2021

Deep Learning Architectures for Amortized Bayesian Inference in Cognitive Modeling.

Stefan T. Radev

PhD thesis, 2021

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany.

Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe

PLoS Comput. Biol., 2021

BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference.

Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

CoRR, 2021

2020

Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks.

Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe

CoRR, 2020

Amortized Bayesian Inference for Models of Cognition.

Stefan T. Radev, Andreas Voss, Eva Marie Wieschen, Paul-Christian Bürkner

CoRR, 2020