Alessandro Manenti

We show that in deep learning models using latent categorical random variables (e.g., VAE, RL, GNNs), the softmax function - commonly used to parameterize the distribution - can hinder training. We theoretically motivate why this is the case using results from Information Geometry. We then propose an alternative parameterization, the catnat function, that - as we prove - has better information-theoretic properties. Extensive empirical evidence demonstrates its effectiveness across diverse settings.

Graph Neural Networks and related models benefit from relational information, but task-relevant relations are often latent. Accurately learning these latent structures and their uncertainty is difficult without direct supervision. In this paper we show that standard loss functions (e.g., MAE, MSE, ...), even when when in a probabilistic form, fail to recover such probabilistic relations. We prove and empirically show that a broad class of losses provides stronger guarantees into learning the correct uncertainty over the latent variables while maintaining predictive accuracy.

Ph.D. student

Phisics of Complex Systems - Master's Double Degree

Phisics - Bachelor's Degree

Lecture for USI — Master Program in Artificial Intelligence (Graph Deep Learning course).