1 citations · 1 across the 3 of their papers we have counts for
7 papers
Improved Sampling Schedules for Discrete Diffusion Models
Alberto Foresti, Mustapha Bounoua, Giulio Franzese +2
Discrete diffusion models have emerged as a powerful paradigm for generative modeling on sequence data; however, the information-theoretic principles governing their reverse proces…
INFO-SEDD: Continuous Time Markov Chains as Scalable Information Metrics Estimators
Alberto Foresti, Giulio Franzese, Pietro Michiardi
Information-theoretic quantities play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific disciplines. However,…
Latent Abstractions in Generative Diffusion Models
Giulio Franzese, Mattia Martini, Giulio Corallo +2
In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of la…
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Giulio Franzese, Dimitrios Milios, Maurizio Filippone +1
We revisit the theoretical properties of Hamiltonian stochastic differential equations (SDES) for Bayesian posterior sampling, and we study the two types of errors that arise from…
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling
Giulio Franzese, Rosa Candela, Dimitrios Milios +2
In this work we define a unified mathematical framework to deepen our understanding of the role of stochastic gradient (SG) noise on the behavior of Markov chain Monte Carlo sampli…
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD
Rosa Candela, Giulio Franzese, Maurizio Filippone +1
Large scale machine learning is increasingly relying on distributed optimization, whereby several machines contribute to the training process of a statistical model. In this work w…