24 citations · 31 across the 4 of their papers we have counts for
5 papers
Latent kinetic Ising models of neural spike trains
Davide Ghio, David Saad
Inferring directed effective interactions from neuronal spike trains is a central inverse problem in statistical physics and computational neuroscience. Kinetic Ising models provid…
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…
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…
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…
Bayes-optimal inference for spreading processes on random networks
D. Ghio, A. L. M. Aragon, I. Biazzo +1
We consider a class of spreading processes on networks, which generalize commonly used epidemic models such as the SIR model or the SIS model with a bounded number of re-infections…