Search Results for author:
Found 16 papers, 11 papers with code
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning
In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains.
Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
To validate our models, we assess whether the inferred parameter trajectories align with the patterns and sequences of the experimental manipulations.
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference
We present multimodal neural posterior estimation (MultiNPE), a method to integrate heterogeneous data from different sources in simulation-based inference with neural networks.
Sensitivity-Aware Amortized Bayesian Inference
In this work, we propose sensitivity-aware amortized Bayesian inference (SA-ABI), a multifaceted approach to efficiently integrate sensitivity analyses into simulation-based inference with neural networks.
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and data.
Simulation-Based Prior Knowledge Elicitation for Parametric Bayesian Models
Our results support the claim that our method is largely independent of the underlying model structure and adaptable to various elicitation techniques, including quantile-based, moment-based, and histogram-based methods.
Finding Competence Regions in Domain Generalization
We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distribution.
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.
A Deep Learning Method for Comparing Bayesian Hierarchical Models
Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions.
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model.
Meta-Uncertainty in Bayesian Model Comparison
Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models.
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains.
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany
Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases.
Amortized Bayesian Inference for Models of Cognition
As models of cognition grow in complexity and number of parameters, Bayesian inference with standard methods can become intractable, especially when the data-generating model is of unknown analytic form.
Amortized Bayesian model comparison with evidential deep learning
This makes the method particularly effective in scenarios where model fit needs to be assessed for a large number of datasets, so that per-dataset inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems.
BayesFlow: Learning complex stochastic models with invertible neural networks
In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
