1 citations · 1 across the 2 of their papers we have counts for
4 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…
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…