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Found 39 papers, 10 papers with code
Graph state-space models
State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made.
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning
Predicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has use cases in chemistry, biology, and medicine.
Message Passing Neural Networks for Hypergraphs
Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects.
Learning Graph Cellular Automata
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.
Learn to Synchronize, Synchronize to Learn
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 learning models.
Input-to-State Representation in linear reservoirs dynamics
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance.
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
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 learn hierarchical representations.
Ranked #1 on
Graph Classification
on Bench-hard
Graph Random Neural Features for Distance-Preserving Graph Representations
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.
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere
Finding such a region requires searching in hyper-parameter space in a sensible way: hyper-parameter configurations marginally outside such a region might yield networks exhibiting fully developed chaos, hence producing unreliable computations.
Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models.
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.
Graph Neural Networks with convolutional ARMA filters
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters.
Ranked #4 on
Skeleton Based Action Recognition
on SBU
Adversarial Autoencoders with Constant-Curvature Latent Manifolds
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 some relevant properties of data, like hierarchy and circularity.
A characterization of the Edge of Criticality in Binary Echo State Networks
Echo State Networks (ESNs) are simplified recurrent neural network models composed of a reservoir and a linear, trainable readout layer.
Interpreting recurrent neural networks behaviour via excitable network attractors
Simulations conducted on a controlled benchmark task confirm the relevance of these attractors for interpreting the behaviour of recurrent neural networks, at least for tasks that involve learning a finite number of stable states and transitions between them.
The Deep Kernelized Autoencoder
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.
Change Point Methods on a Sequence of Graphs
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 in the stochastic process generating the graphs.
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders
In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS.
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams.
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals with the aim of predicting the onset of Paroxysmal Atrial Fibrillation (PAF).
Concept Drift and Anomaly Detection in Graph Streams
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains.
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space.
Multiplex visibility graphs to investigate recurrent neural networks dynamics
We show that topological properties of such a multiplex reflect important features of RNN dynamics and are used to guide the tuning procedure.
One-class classifiers based on entropic spanning graphs
The final partition is derived by exploiting a criterion based on mutual information minimization.
Determination of the edge of criticality in echo state networks through Fisher information maximization
In this paper, we aim at addressing this issue by proposing a theoretically motivated, unsupervised method based on Fisher information for determining the edge of criticality in recurrent neural networks.
Investigating echo state networks dynamics by means of recurrence analysis
We verify that the determination of the edge of stability provided by such RQA measures is more accurate than two well-known criteria based on the Jacobian matrix of the reservoir.
Data-driven detrending of nonstationary fractal time series with echo state networks
We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process.
Discrimination and characterization of Parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures
The subjects belong to two different groups, formed by four and eight subjects with, respectively, high- and low-amplitude rest tremors.
On the impact of topological properties of smart grids in power losses optimization problems
In this paper, we face the problem of joint optimization of both topology and network parameters in a real smart grid.
Building pattern recognition applications with the SPARE library
This paper presents the SPARE C++ library, an open source software tool conceived to build pattern recognition and soft computing systems.
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
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 parametric dissimilarity measure yielding compact and separated clusters.
Designing labeled graph classifiers by exploiting the Rényi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence.
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 named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects.
Characterization of graphs for protein structure modeling and recognition of solubility
This paper deals with the relations among structural, topological, and chemical properties of the E. Coli proteome from the vantage point of the solubility/aggregation propensity of proteins.
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which provides the unique possibility to analyze the relative aggregation/folding propensity of the elements of the entire Escherichia coli (E. coli) proteome in a cell-free standardized microenvironment.
Entropic one-class classifiers
The one-class classification problem is a well-known research endeavor in pattern recognition.
Data granulation by the principles of uncertainty
The proposed framework is conceived (i) to offer a guideline for the synthesis of information granules and (ii) to build a groundwork to compare and quantitatively judge over different data granulation procedures.
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e. g., cables and related insulation, transformers, breakers and so on).
