Publications

The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot …

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. …

While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget …

Few-shot graph classification is a novel yet promising emerging research field that still lacks the soundness of well-established …

Aligning the visual and language spaces requires to train deep neural networks from scratch on giant multimodal datasets; CLIP trains …

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their …

Spectral geometric methods have brought revolutionary changes to the field of geometry processing. Of particular interest is the study …

We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences – e.g. …

In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds. The proposed …

The paper investigates if and how the topology of the peer-assessment network can affect the performance of the Bayesian model adopted …

The paper presents a study of the performance variations of the Bayesian model of peer-assessment implemented in OpenAnswer, in terms …