3 citations · 7 across the 6 of their papers we have counts for
10 papers · 1 filter
CogFormer: Learn All Your Models Once
Jerry M. Huang, Lukas Schumacher, Niek Stevenson +1
Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fit complex models that were previously…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian inference (ABI) offers fast, scalable approximations to posterior densities by training neural surrogates on data simulated from the statistical model. However,…
Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Šimon Kucharský, Aayush Mishra, Daniel Habermann +2
Amortized Bayesian model comparison (BMC) enables fast probabilistic ranking of models via simulation-based training of neural surrogates. However, the accuracy of neural surrogate…
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
Lasse Elsemüller, Valentin Pratz, Mischa von Krause +3
Neural networks are fragile when confronted with data that significantly deviates from their training distribution. This is true in particular for simulation-based inference method…
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
Aayush Mishra, Daniel Habermann, Marvin Schmitt +2
Amortized Bayesian inference (ABI) with neural networks can solve probabilistic inverse problems orders of magnitude faster than classical methods. However, ABI is not yet sufficie…
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
Yufei Wu, Stefan T. Radev, Francis Tuerlinckx
Contaminant observations and outliers often cause problems when estimating the parameters of cognitive models, which are statistical models representing cognitive processes. In thi…