2 papers
stat.ML2025
Supervised Learning of Random Neural Architectures Structured by Latent Random Fields on Compact Boundaryless Multiply-Connected Manifolds
Christian Soize
This paper introduces a new probabilistic framework for supervised learning in neural systems. It is designed to model complex, uncertain systems whose random outputs are strongly…
stat.ME2025
Estimating Intractable Posterior Distributions through Gaussian Process regression and Metropolis-adjusted Langevin procedure
Guillaume Perrin, Romain Jorge Do Marco, Christian Soize +1
Numerical simulations are crucial for modeling complex systems, but calibrating them becomes challenging when data are noisy or incomplete and likelihood evaluations are computatio…