Search Results for author:
Found 4 papers, 3 papers with code
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
Neural networks are fragile when confronted with data that significantly deviates from their training distribution.
Consistency Models for Scalable and Fast Simulation-Based Inference
Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models.
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
