research
∙
08/24/2023
Probabilistic load forecasting with Reservoir Computing
Some applications of deep learning require not only to provide accurate ...
share
research
∙
04/14/2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Subgraph-enhanced graph neural networks (SGNN) can increase the expressi...
research
∙
04/04/2023
The expressive power of pooling in Graph Neural Networks
In Graph Neural Networks (GNNs), hierarchical pooling operators generate...
research
∙
11/11/2022
Clustering with Total Variation Graph Neural Networks
Graph Neural Networks (GNNs) are deep learning models designed to proces...
research
∙
09/16/2022
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been int...
research
∙
09/14/2022
Scalable Spatiotemporal Graph Neural Networks
Neural forecasting of spatiotemporal time series drives both research an...
research
∙
07/18/2022
Simplifying Clustering with Graph Neural Networks
The objective functions used in spectral clustering are usually composed...
research
∙
03/30/2022
Recognition of polar lows in Sentinel-1 SAR images with deep learning
In this paper, we explore the possibility of detecting polar lows in C-b...
research
∙
03/10/2022
Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case
The energy market relies on forecasting capabilities of both demand and ...
research
∙
02/17/2022
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
This paper presents a novel probabilistic forecasting method called ense...
research
∙
11/03/2021
Power Flow Balancing with Decentralized Graph Neural Networks
We propose an end-to-end framework based on a Graph Neural Network (GNN)...
research
∙
10/11/2021
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural netw...
research
∙
08/16/2021
Detecting and interpreting faults in vulnerable power grids with machine learning
Unscheduled power disturbances cause severe consequences both for custom...
research
∙
04/10/2021
Pyramidal Reservoir Graph Neural Network
We propose a deep Graph Neural Network (GNN) model that alternates two t...
research
∙
06/24/2020
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...
research
∙
01/13/2020
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
Image translation with convolutional neural networks has recently been u...
research
∙
10/24/2019
Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
In graph neural networks (GNNs), pooling operators compute local summari...
research
∙
10/11/2019
Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks
Knowledge about frequency and location of snow avalanche activity is ess...
research
∙
07/10/2019
Time series cluster kernels to exploit informative missingness and incomplete label information
The time series cluster kernel (TCK) provides a powerful tool for analys...
research
∙
06/30/2019
Mincut pooling in Graph Neural Networks
The advance of node pooling operations in a Graph Neural Network (GNN) h...
research
∙
02/20/2019
Noisy multi-label semi-supervised dimensionality reduction
Noisy labeled data represent a rich source of information that often are...
research
∙
01/05/2019
Graph Neural Networks with convolutional ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
research
∙
01/05/2019
Graph Neural Networks with distributed ARMA filters
Recent graph neural networks implement convolutional layers based on pol...
research
∙
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...
research
∙
03/21/2018
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 m...
research
∙
03/21/2018
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
research
∙
01/21/2018
Time series kernel similarities for predicting Paroxysmal Atrial Fibrillation from ECGs
We tackle the problem of classifying Electrocardiography (ECG) signals w...
research
∙
12/10/2017
A novel algorithm for online inexact string matching and its FPGA implementation
Accelerating inexact string matching procedures is of utmost importance ...
research
∙
11/22/2017
Identifying user habits through data mining on call data records
In this paper we propose a framework for identifying patterns and regula...
research
∙
11/17/2017
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 ...
research
∙
11/17/2017
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate ti...
research
∙
11/17/2017
Bidirectional deep-reservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
research
∙
11/17/2017
Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
research
∙
10/20/2017
Learning compressed representations of blood samples time series with missing data
Clinical measurements collected over time are naturally represented as m...
research
∙
05/11/2017
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
The key component in forecasting demand and consumption of resources in ...
research
∙
04/03/2017
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time ser...
research
∙
02/23/2017
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
In this paper, we propose PCKID, a novel, robust, kernel function for sp...
research
∙
02/08/2017
Deep Kernelized Autoencoders
In this paper we introduce the deep kernelized autoencoder, a neural net...
research
∙
01/18/2017
Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture de...
research
∙
09/10/2016
Multiplex visibility graphs to investigate recurrent neural networks dynamics
A recurrent neural network (RNN) is a universal approximator of dynamica...
research
∙
08/16/2016
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Networ...
research
∙
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...
research
∙
10/24/2015
