5 papers
An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
Maximilian Dax, Theo Heimel, Gilles Louppe
Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and…
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
Gérôme Andry, Sacha Lewin, François Rozet +6
Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we int…
Learning Diffusion Priors from Observations by Expectation Maximization
François Rozet, Gérôme Andry, François Lanusse +1
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, whi…
Simulation-Based Inference Benchmark for Weak Lensing Cosmology
Justine Zeghal, Denise Lanzieri, François Lanusse +5
Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmolo…
An implementation of neural simulation-based inference for parameter estimation in ATLAS
ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation witho…