439 citations · 2k across the 50 of their papers we have counts for
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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…
Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks
Filippo Maria Bianchi, Jakob Grahn, Markus Eckerstorfer +2
Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activ…
Unsupervised Image Regression for Heterogeneous Change Detection
Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser +1
Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the…
Time series cluster kernels to exploit informative missingness and incomplete label information
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +2
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in…
Spectral Clustering with Graph Neural Networks for Graph Pooling
Filippo Maria Bianchi, Daniele Grattarola, Cesare Alippi
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement pooling op…
Noisy multi-label semi-supervised dimensionality reduction
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +1
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not ac…
Deep Divergence-Based Approach to Clustering
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +3
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminati…
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…