28 citations · 64 across the 9 of their papers we have counts for
3 papers · 1 filter
Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
Jonathan Renusch, Benjamin Huth, Daniel Murnane +9
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linea…
It Just Takes Two: Scaling Amortized Inference to Large Sets
Antoine Wehenkel, Michael Kagan, Lukas Heinrich +1
Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In many of these applications, the…
Gradient estimators for parameter inference in discrete stochastic kinetic models
Ludwig Burger, Annalena Kofler, Lukas Heinrich +1
Stochastic kinetic models are ubiquitous in physics, yet inferring their parameters from experimental data remains challenging. For deterministic models, parameter inference often…