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07/07/2023
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Time series are the primary data type used to record dynamic system meas...
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05/30/2023
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Existing relationships among time series can be exploited as inductive b...
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04/11/2023
Feudal Graph Reinforcement Learning
We focus on learning composable policies to control a variety of physica...
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03/26/2023
Relational Inductive Biases for Object-Centric Image Generation
Conditioning image generation on specific features of the desired output...
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03/21/2023
Graph Kalman Filters
The well-known Kalman filters model dynamical systems by relying on stat...
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02/08/2023
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time ...
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02/03/2023
Where and How to Improve Graph-based Spatio-temporal Predictors
This paper introduces a novel residual correlation analysis, called AZ-a...
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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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10/10/2022
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
In real-world applications, the process generating the data might suffer...
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09/14/2022
Scalable Spatiotemporal Graph Neural Networks
Neural forecasting of spatiotemporal time series drives both research an...
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05/26/2022
Sparse Graph Learning for Spatiotemporal Time Series
Outstanding achievements of graph neural networks for spatiotemporal tim...
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05/26/2022
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
Modeling multivariate time series as temporal signals over a (possibly d...
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04/23/2022
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
We present the first whiteness test for graphs, i.e., a whiteness test f...
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11/29/2021
A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration
The design of efficient hardware accelerators for high-throughput data-p...
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11/16/2021
Graph neural network-based fault diagnosis: a review
Graph neural network (GNN)-based fault diagnosis (FD) has received incre...
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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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10/11/2021
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural netw...
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07/31/2021
Multivariate Time Series Imputation by Graph Neural Networks
Dealing with missing values and incomplete time series is a labor-intens...
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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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06/22/2020
Graph Neural Networks in TensorFlow and Keras with Spektral
In this paper we present Spektral, an open-source Python library for bui...
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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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03/20/2020
Deep Reinforcement Learning with Weighted Q-Learning
Overestimation of the maximum action-value is a well-known problem that ...
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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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08/02/2019
Distributed Deep Convolutional Neural Networks for the Internet-of-Things
Due to the high demand in computation and memory, deep learning solution...
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07/22/2019
Deep Learning for Time Series Forecasting: The Electric Load Case
Management and efficient operations in critical infrastructure such as S...
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06/30/2019
Mincut pooling in Graph Neural Networks
The advance of node pooling operations in a Graph Neural Network (GNN) h...
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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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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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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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05/03/2018
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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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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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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10/16/2015