Filippo Bianchi

Explainability in subgraphs-enhanced Graph Neural Networks – Proceedings of the Northern Lights Deep Learning Workshop 2023

Scalable Spatiotemporal Graph Neural Networks – Proceedings of the AAAI Conference on Artificial Intelligence 2023

Simplifying Clustering with Graph Neural Networks – Proceedings of the Northern Lights Deep Learning Workshop 2023

Power Flow Balancing With Decentralized Graph Neural Networks – IEEE Transactions on Power Systems 2022

Understanding Pooling in Graph Neural Networks – IEEE Transactions on Neural Networks and Learning Systems 2022

Recognition of Polar Lows in Sentinel-1 SAR Images With Deep Learning – IEEE Transactions on Geoscience and Remote Sensing 2022

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting – IEEE Transactions on Neural Networks and Learning Systems 2022

Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images – PLOS ONE 2022

Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case – Energy Conversion and Management: X 2022

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images – IEEE Transactions on Neural Networks and Learning Systems 2022

Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case – Energies 2022

Detecting and Interpreting Faults in Vulnerable Power Grids With Machine Learning – IEEE Access 2021

A Deep Learning Approach for Automated Fault Detection on Solar Modules Using Image Composites – 2021

Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms – 2021

Pyramidal Reservoir Graph Neural Network – Neurocomputing 2021

Estimation of Excess Mortality and Years of Life Lost to COVID-19 in Norway and Sweden between March and November 2020 – International Journal of Environmental Research and Public Health 2021

Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection – IEEE Transactions on Geoscience and Remote Sensing 2021

Time series cluster kernels to exploit informative missingness and incomplete label information – Pattern Recognition 2021

Graph Neural Networks With Convolutional ARMA Filters – IEEE Transactions on Pattern Analysis and Machine Intelligence 2021

Predicting Energy Demand in Semi-Remote Arctic Locations – Energies 2021

Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series – IEEE Transactions on Neural Networks and Learning Systems 2021

Spectral Clustering with Graph Neural Networks for Graph Pooling – Proceedings of Machine Learning Research (PMLR) 2020

Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks – Proceedings of the Northern Lights Deep Learning Workshop 2020

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling – IEEE Transactions on Neural Networks and Learning Systems 2020

Intervention Fatigue is the Primary Cause of Strong Secondary Waves in the COVID-19 Pandemic – International Journal of Environmental Research and Public Health 2020

Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks – IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020

Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning – Remote Sensing 2020

Non-iterative Learning Approaches and Their Applications – Cognitive Computation 2020

Learning representations of multivariate time series with missing data – Pattern Recognition 2019

Learning representations of multivariate time series with missing data – Pattern Recognition 2019

A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation – Cognitive Computation 2019

Unsupervised Image Regression for Heterogeneous Change Detection – IEEE Transactions on Geoscience and Remote Sensing 2019

Noisy multi-label semi-supervised dimensionality reduction – Pattern Recognition 2019

Deep divergence-based approach to clustering – Neural Networks 2019

Outlier classification using autoencoders: Application for fluctuation driven flows in fusion plasmas – Review of Scientific Instruments 2019

Deep learning for graphs – 2018

Pyramidal Graph Echo State Networks – 2018

Remote sensing image regression for heterogeneous change detection – 2018

Time Series Kernel Similarities for Predicting Paroxysmal Atrial Fibrillation from ECGs – Proceedings of the International Joint Conference on Neural Networks 2018

Bidirectional deep-readout echo state networks – 2018

Learning compressed representations of blood samples time series with missing data – 2018

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks – 2018

The deep kernelized autoencoder – Applied Soft Computing 2018

On the interpretation and characterization of echo state networks dynamics: A complex systems perspective – 2018

Time series cluster kernel for learning similarities between multivariate time series with missing data – Pattern Recognition 2017

The time series cluster kernel – 2017

Critical echo state network dynamics by means of Fisher information maximization – 2017

Deep divergence-based clustering – 2017

Temporal overdrive recurrent neural network – 2017

Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis – Springer 2017

A clustering approach to heterogeneous change detection – 2017

Local short term electricity load forecasting: Automatic approaches – Proceedings of the International Joint Conference on Neural Networks 2017

Deep kernelized autoencoders – Lecture Notes in Computer Science (LNCS) 2017

Spectral clustering using PCKID ? A probabilistic cluster kernel for incomplete data – Lecture Notes in Computer Science (LNCS) 2017

Multiplex visibility graphs to investigate recurrent neural network dynamics – Scientific Reports 2017

Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization – IEEE Transactions on Neural Networks and Learning Systems 2017

Data-driven detrending of nonstationary fractal time series with echo state networks – Information Sciences 2017

Training Echo State Networks with Regularization Through Dimensionality Reduction – Cognitive Computation 2017

Identifying user habits through data mining on call data records – Engineering Applications of Artificial Intelligence 2016

Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis – IEEE Transactions on Neural Networks and Learning Systems 2016

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