22 citations · 44 across the 7 of their papers we have counts for
9 papers
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…
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…
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
Theogene Habineza, Antônio H. Ribeiro, Daniel Gedon +3
Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of…
Invertible Kernel PCA with Random Fourier Features
Daniel Gedon, Antôni H. Ribeiro, Niklas Wahlström +1
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation. The prevailing method to…
Deep networks for system identification: a Survey
Gianluigi Pillonetto, Aleksandr Aravkin, Daniel Gedon +3
Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results i…
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
Philipp Von Bachmann, Daniel Gedon, Fredrik K. Gustafsson +5
Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outc…
Distributed training and scalability for the particle clustering method UCluster
Olga Sunneborn Gudnadottir, Daniel Gedon, Colin Desmarais +3
In recent years, machine-learning methods have become increasingly important for the experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger sy…
Deep State Space Models for Nonlinear System Identification
Daniel Gedon, Niklas Wahlström, Thomas B. Schön +1
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. Th…