753 citations · 3k across the 55 of their papers we have counts for
10 papers · 1 filter
Adversarial Autoencoders with Constant-Curvature Latent Manifolds
Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Constant-curvature Riemannian manifolds (CCMs) have been shown to be ideal embedding spaces in many application domains, as their non-Euclidean geometry can naturally account for s…
Coherent Manipulation of Orbital Feshbach Molecules of Two-Electron Atoms
G. Cappellini, L. F. Livi, L. Franchi +5
Ultracold molecules have experienced increasing attention in recent years. Compared to ultracold atoms, they possess several unique properties that make them perfect candidates for…
A characterization of the Edge of Criticality in Binary Echo State Networks
Pietro Verzelli, Lorenzo Livi, Cesare Alippi
Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer. The reservoir is tunable by some hyper-para…
The Deep Kernelized Autoencoder
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +2
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network. However, it is not always clear what kind of properties of…
Interpreting recurrent neural networks behaviour via excitable network attractors
Andrea Ceni, Peter Ashwin, Lorenzo Livi
Introduction: Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides metho…
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
Daniele Zambon, Lorenzo Livi, Cesare Alippi
Mapping complex input data into suitable lower dimensional manifolds is a common procedure in machine learning. This step is beneficial mainly for two reasons: (1) it reduces the d…
Change Point Methods on a Sequence of Graphs
Daniele Zambon, Cesare Alippi, Lorenzo Livi
Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity i…
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
Daniele Grattarola, Daniele Zambon, Cesare Alippi +1
The space of graphs is often characterised by a non-trivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding tech…
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen +2
Learning compressed representations of multivariate time series (MTS) facilitates data analysis in the presence of noise and redundant information, and for a large number of variat…
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
Filippo Maria Bianchi, Lorenzo Livi, Alberto Ferrante +2
We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF). Atrial fibrillation is the most…