6 papers · 1 filter
Cartan flow matching
Francesco Ruscelli, Ferdinando Zanchetta, Rita Fioresi
We introduce Cartan flow matching, a general framework for training flow matching models on Riemannian symmetric spaces, i.e. Riemannian manifolds with the property that at any poi…
Sheaf Neural Networks and biomedical applications
Aneeqa Mehrab, Jan Willem Van Looy, Pietro Demurtas +5
The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biom…
A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
Pietro Demurtas, Ferdinando Zanchetta, Giovanni Perini +1
Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactio…
Graph Neural Networks and Time Series as Directed Graphs for Quality Recognition
Angelica Simonetti, Ferdinando Zanchetta
Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled with existing algorithms as Temporal Convolutional Networks and Recurrent Neural Networks. In…
Geometric Deep Learning: a Temperature Based Analysis of Graph Neural Networks
M. Lapenna, F. Faglioni, F. Zanchetta +1
We examine a Geometric Deep Learning model as a thermodynamic system treating the weights as non-quantum and non-relativistic particles. We employ the notion of temperature previou…
Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists
R. Fioresi, F. Zanchetta
In this expository paper we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successfull algorithms of Deep L…