1 citations · 1 across the 3 of their papers we have counts for
9 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…
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…