Search Results for author:
Found 43 papers, 18 papers with code
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
The input time series are progressively coarsened over time and space, obtaining a pool of representations that capture heterogeneous temporal and spatial dynamics.
Probabilistic load forecasting with Reservoir Computing
For this reason, point forecasts are not enough hence it is necessary to adopt methods that provide an uncertainty quantification.
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework.
Total Variation Graph Neural Networks
Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation.
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman isomorphism test.
Scalable Spatiotemporal Graph Neural Networks
The training procedure can then be parallelized node-wise by sampling the node embeddings without breaking any dependency, thus enabling scalability to large networks.
Simplifying Clustering with Graph Neural Networks
The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the graph and; ii) a term that balances the clustering partition and helps avoiding degenerate solutions.
Recognition of polar lows in Sentinel-1 SAR images with deep learning
The dataset is used to train a deep learning model to classify the labeled images.
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
Then, deep learning models are used to make probabilistic forecasts of day-ahead power generation from a wind power plant located in Northern Norway.
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data.
Power Flow Balancing with Decentralized Graph Neural Networks
We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids.
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs.
Detecting and interpreting faults in vulnerable power grids with machine learning
The proposed approach allows to gain detailed insights on the occurrence of a specific fault, which are valuable for the distribution system operators to implement strategies to prevent and mitigate power disturbances.
Pyramidal Reservoir Graph Neural Network
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers.
Large-scale detection and categorization of oil spills from SAR images with deep learning
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale.
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection.
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
Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks
A visual inspection of the results generated by our deep learning model shows that only small avalanches are undetected, while some avalanches that were originally not labelled by the human expert are discovered.
Mincut Pooling in Graph Neural Networks
For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e. g., a graph classification loss), and, thanks to the minCut objective, also on the connectivity structure of the graph.
Time series cluster kernels to exploit informative missingness and incomplete label information
To overcome this limitation, we present a kernel capable of exploiting the potentially rich information in the missing values and patterns, as well as the information from the observed data.
Spectral Clustering with Graph Neural Networks for Graph Pooling
Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.
Noisy multi-label semi-supervised dimensionality reduction
With the proposed Noisy multi-label semi-supervised dimensionality reduction (NMLSDR) method, the noisy multi-labels are denoised and unlabeled data are labeled simultaneously via a specially designed label propagation algorithm.
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
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.
Reservoir computing approaches for representation and classification of multivariate time series
The architectures are compared to other MTS classifiers, including deep learning models and time series kernels.
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient.
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).
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate time series.
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete.
Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data.
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet.
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel.
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data.
A clustering approach to heterogeneous change detection
Change detection in heterogeneous multitemporal satellite images is a challenging and still not much studied topic in remote sensing and earth observation.
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.
Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics.
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.
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to predict real valued time-series.
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.
Position paper: a general framework for applying machine learning techniques in operating room
In this position paper we describe a general framework for applying machine learning and pattern recognition techniques in healthcare.
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.
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.
