753 citations · 3k across the 55 of their papers we have counts for
17 papers · 1 filter
Anti-Collapse Dynamics and the Emergence of Multi-Time-Scale Learning in Recurrent Neural Networks
Lorenzo Livi
Long-range learning is hard for recurrent networks trained with stochastic gradient descent, because the influence of a past input fades with the lag , and if it fades too fa…
Learnability Window in Gated Recurrent Neural Networks
Lorenzo Livi
We develop a statistical theory of temporal learnability in recurrent neural networks, quantifying the maximal temporal horizon over which gradient-based learning c…
Graph State-Space Models and Latent Relational Inference
Daniele Zambon, Andrea Cini, Cesare Alippi
State-space models effectively model multivariate time series by updating over time a representation of the system state from which predictions are made. The state representation i…
Message Passing Neural Networks for Hypergraphs
Sajjad Heydari, Lorenzo Livi
Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph n…
Learning Graph Cellular Automata
Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Cellular automata (CA) are a class of computational models that exhibit rich dynamics emerging from the local interaction of cells arranged in a regular lattice. In this work we fo…
Learn to Synchronize, Synchronize to Learn
Pietro Verzelli, Cesare Alippi, Lorenzo Livi
In recent years, the machine learning community has seen a continuous growing interest in research aimed at investigating dynamical aspects of both training procedures and machine…
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that…
Graph Random Neural Features for Distance-Preserving Graph Representations
Daniele Zambon, Cesare Alippi, Lorenzo Livi
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks. The embedding natura…
Autoregressive Models for Sequences of Graphs
Daniele Zambon, Daniele Grattarola, Lorenzo Livi +1
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very g…
Graph Neural Networks with convolutional ARMA filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi +1
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired…
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…
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…
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…
Concept Drift and Anomaly Detection in Graph Streams
Daniele Zambon, Cesare Alippi, Lorenzo Livi
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains. Here, we consider stochastic processes generating graphs and propose…
One-class classifiers based on entropic spanning graphs
Lorenzo Livi, Cesare Alippi
One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic sp…
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Filippo Maria Bianchi, Enrico Maiorino, Lorenzo Livi +2
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted pa…