5 citations · 19 across the 15 of their papers we have counts for
10 papers · 1 filter
Mixed neural posterior estimation for simulators with discrete and continuous parameters
Jan Boelts, Cornelius Schröder, Jonas Beck +3
Neural Posterior Estimation (NPE) enables rapid parameter inference for complex simulators with intractable likelihoods. NPE trains an inference network to estimate a probability d…
A Probabilistic Framework for LLM-Based Model Discovery
Stefan Wahl, Raphaela Schenk, Ali Farnoud +2
Automated methods for discovering mechanistic simulator models from observational data offer a promising path toward accelerating scientific progress. Such methods often take the f…
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
Lisa Haxel, Jaivardhan Kapoor, Ulf Ziemann +1
Brain-computer interfaces (BCIs) suffer from accuracy degradation as neural signals drift over time and vary across users, requiring frequent recalibration that limits practical de…
FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators
Guy Moss, Leah Sophie Muhle, Reinhard Drews +2
Simulation-based inference (SBI) is an established approach for performing Bayesian inference on scientific simulators. SBI so far works best on low-dimensional parametric models.…
Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
Julius Vetter, Manuel Gloeckler, Daniel Gedon +1
Simulation-based inference (SBI) offers a flexible and general approach to performing Bayesian inference: In SBI, a neural network is trained on synthetic data simulated from a mod…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Compositional simulation-based inference for time series
Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu +1
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likeliho…
Inferring stochastic low-rank recurrent neural networks from neural data
Matthijs Pals, A Erdem Sağtekin, Felix Pei +2
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should idea…
All-in-one simulation-based inference
Manuel Gloeckler, Michael Deistler, Christian Weilbach +2
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference…
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Sebastian Bischoff, Alana Darcher, Michael Deistler +18
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, prote…