83 citations · 343 across the 46 of their papers we have counts for
48 papers
Can We Trust the Judges? Validation of Factuality Evaluation Methods via Answer Perturbation
Sarra Gharsallah, Adele Robaldo, Mariia Tokareva +5
Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy? Despite the rise of factuali…
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models
Ba-Hien Tran, Giulio Franzese, Pietro Michiardi +1
Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impress…
Continuous-Time Functional Diffusion Processes
Giulio Franzese, Giulio Corallo, Simone Rossi +3
We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework…
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning
Raphael Azorin, Massimo Gallo, Alessandro Finamore +2
While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from…
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Giulio Franzese, Simone Rossi, Lixuan Yang +4
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have sta…
Safer Autonomous Driving in a Stochastic, Partially-Observable Environment by Hierarchical Contingency Planning
Ugo Lecerf, Christelle Yemdji-Tchassi, Pietro Michiardi
When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and b…