1 citations · 1 across the 9 of their papers we have counts for
10 papers
DIPHINE: Diffusion-based -ID Neural Estimator
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Uncovering the true informational architecture of real-world complex systems requires disentangling how their components uniquely store, redundantly share, and synergistically inte…
Relative Entropy Estimation in Function Space: Theory and Applications to Trajectory Inference
Chao Wang, Luca Nepote, Giulio Franzese +1
Trajectory Inference (TI) seeks to recover latent dynamical processes from snapshot data, where only independent samples from time-indexed marginals are observed. In applications s…
TENDE: Transfer Entropy Neural Diffusion Estimation
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems ana…
Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese +2
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by the…
RFMI: Estimating Mutual Information on Rectified Flow for Text-to-Image Alignment
Chao Wang, Giulio Franzese, Alessandro Finamore +1
Rectified Flow (RF) models trained with a Flow matching framework have achieved state-of-the-art performance on Text-to-Image (T2I) conditional generation. Yet, multiple benchmarks…
Learning to Match Unpaired Data with Minimum Entropy Coupling
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
Multimodal data is a precious asset enabling a variety of downstream tasks in machine learning. However, real-world data collected across different modalities is often not paired,…
In Praise of Stubbornness: An Empirical Case for Cognitive-Dissonance Aware Continual Update of Knowledge in LLMs
Simone Clemente, Zied Ben Houidi, Alexis Huet +3
Through systematic empirical investigation, we uncover a fundamental and concerning property of Large Language Models: while they can safely learn facts that don't contradict their…
Information Theoretic Text-to-Image Alignment
Chao Wang, Giulio Franzese, Alessandro Finamore +2
Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a labori…
ADROIT6G DAI-driven Open and Programmable Architecture for 6G Networks
Christophoros Christophorou, Iacovos Ioannou, Vasos Vassiliou +10
In the upcoming 6G era, mobile networks must deal with more challenging applications (e.g., holographic telepresence and immersive communication) and meet far more stringent applic…
SI: Score-based O-INFORMATION Estimation
Mustapha Bounoua, Giulio Franzese, Pietro Michiardi
The analysis of scientific data and complex multivariate systems requires information quantities that capture relationships among multiple random variables. Recently, new informati…