439 citations · 1.4k across the 66 of their papers we have counts for
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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…
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…
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…