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01/04/2023
Graph state-space models
State-space models constitute an effective modeling tool to describe mul...
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05/06/2022
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning
Predicting structural and energetic properties of a molecular system is ...
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03/31/2022
Message Passing Neural Networks for Hypergraphs
Hypergraph representations are both more efficient and better suited to ...
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10/27/2021
Learning Graph Cellular Automata
Cellular automata (CA) are a class of computational models that exhibit ...
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10/06/2020
Learn to Synchronize, Synchronize to Learn
In recent years, the machine learning community has seen a continuous gr...
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03/24/2020
Input representation in recurrent neural networks dynamics
Reservoir computing is a popular approach to design recurrent neural net...
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10/24/2019
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
In graph neural networks (GNNs), pooling operators compute local summari...
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09/09/2019
Distance-Preserving Graph Embeddings from Random Neural Features
We present Graph Random Neural Features (GRNF), a novel embedding method...
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03/27/2019
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Among the various architectures of Recurrent Neural Networks, Echo State...
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03/18/2019
Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs...
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02/13/2019
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning rep...
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01/05/2019
Graph Neural Networks with convolutional ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
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01/05/2019
Graph Neural Networks with distributed ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
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12/11/2018
Adversarial Autoencoders with Constant-Curvature Latent Manifolds
Constant-curvature Riemannian manifolds (CCMs) have been shown to be ide...
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10/03/2018
A characterization of the Edge of Criticality in Binary Echo State Networks
Echo State Networks (ESNs) are simplified recurrent neural network model...
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07/27/2018
Interpreting RNN behaviour via excitable network attractors
Machine learning has become a basic tool in scientific research and for ...
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07/19/2018
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the i...
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05/18/2018
Change Point Methods on a Sequence of Graphs
The present paper considers a finite sequence of graphs, e.g., coming fr...
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05/16/2018
Learning Graph Embeddings on Constant-Curvature Manifolds for Change Detection in Graph Streams
The space of graphs is characterized by a non-trivial geometry, which of...
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05/09/2018
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
Learning compressed representations of multivariate time series (MTS) fa...
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05/03/2018
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
Mapping complex input data into suitable lower dimensional manifolds is ...
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01/21/2018
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals w...
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06/21/2017
Concept Drift and Anomaly Detection in Graph Streams
Graph representations offer powerful and intuitive ways to describe data...
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02/08/2017
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
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09/10/2016
Multiplex visibility graphs to investigate recurrent neural networks dynamics
A recurrent neural network (RNN) is a universal approximator of dynamica...
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04/08/2016
One-class classifiers based on entropic spanning graphs
One-class classifiers offer valuable tools to assess the presence of out...
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03/11/2016
Determination of the edge of criticality in echo state networks through Fisher information maximization
It is a widely accepted fact that the computational capability of recurr...
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10/24/2015
Data-driven detrending of nonstationary fractal time series with echo state networks
In this paper, we propose a novel data-driven approach for removing tren...
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04/10/2015
Discrimination and characterization of Parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures
In this paper, we analyze 48 signals of rest tremor velocity related to ...
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01/19/2015
On the impact of topological properties of smart grids in power losses optimization problems
Power losses reduction is one of the main targets for any electrical ene...
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10/20/2014
Building pattern recognition applications with the SPARE library
This paper presents the SPARE C++ library, an open source software tool ...
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08/22/2014
Designing labeled graph classifiers by exploiting the Rényi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common ...
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08/17/2014
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system nam...
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07/30/2014
Characterization of graphs for protein structure modeling and recognition of solubility
This paper deals with the relations among structural, topological, and c...
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07/28/2014
Entropic one-class classifiers
The one-class classification problem is a well-known research endeavor i...
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07/26/2014
Data granulation by the principles of uncertainty
Researches in granular modeling produced a variety of mathematical model...
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07/25/2014