3 papers
stat.ML2025
Inference in Spreading Processes with Neural-Network Priors
Davide Ghio, Fabrizio Boncoraglio, Lenka Zdeborová
Stochastic processes on graphs are a powerful tool for modelling complex dynamical systems such as epidemics. A recent line of work focused on the inference problem where one aims…
cond-mat.dis-nn2023
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
Davide Ghio, Yatin Dandi, Florent Krzakala +1
Recent years witnessed the development of powerful generative models based on flows, diffusion or autoregressive neural networks, achieving remarkable success in generating data fr…
cond-mat.dis-nn2023
High-Dimensional Non-Convex Landscapes and Gradient Descent Dynamics
Tony Bonnaire, Davide Ghio, Kamesh Krishnamurthy +3
In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Ou…