Filippo Maria Bianchi

Orcid: 0000-0002-7145-3846

According to our database1, Filippo Maria Bianchi authored at least 77 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting.
IEEE Trans. Neural Networks Learn. Syst., July, 2024

Understanding Pooling in Graph Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images.
IEEE Trans. Neural Networks Learn. Syst., January, 2024

Interpreting Temporal Graph Neural Networks with Koopman Theory.
CoRR, 2024

MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks.
CoRR, 2024

Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Probabilistic Load Forecasting With Reservoir Computing.
IEEE Access, 2023

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

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

The expressive power of pooling in Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Total Variation Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023

Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Scalable Spatiotemporal Graph Neural Networks.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling.
IEEE Trans. Neural Networks Learn. Syst., 2022

Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection.
IEEE Trans. Geosci. Remote. Sens., 2022

Recognition of Polar Lows in Sentinel-1 SAR Images With Deep Learning.
IEEE Trans. Geosci. Remote. Sens., 2022

Graph Neural Networks With Convolutional ARMA Filters.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Pyramidal Reservoir Graph Neural Network.
Neurocomputing, 2022

Clustering with Total Variation Graph Neural Networks.
CoRR, 2022

Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: An Arctic case.
CoRR, 2022

2021
Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series.
IEEE Trans. Neural Networks Learn. Syst., 2021

Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

Time series cluster kernels to exploit informative missingness and incomplete label information.
Pattern Recognit., 2021

Power Flow Balancing with Decentralized Graph Neural Networks.
CoRR, 2021

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

Detecting the Linear and Non-linear Causal Links for Disturbances in the Power Grid.
Proceedings of the Intelligent Technologies and Applications, 2021

Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms.
Proceedings of the IEEE International Conference on Consumer Electronics, 2021

Deep learning for graphs.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020
Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning.
Remote. Sens., 2020

A Novel Algorithm for Online Inexact String Matching and its FPGA Implementation.
Cogn. Comput., 2020

Non-iterative Learning Approaches and Their Applications.
Cogn. Comput., 2020

Spectral Clustering with Graph Neural Networks for Graph Pooling.
Proceedings of the 37th International Conference on Machine Learning, 2020

Pyramidal Graph Echo State Networks.
Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019
Unsupervised Image Regression for Heterogeneous Change Detection.
IEEE Trans. Geosci. Remote. Sens., 2019

Noisy multi-label semi-supervised dimensionality reduction.
Pattern Recognit., 2019

Learning representations of multivariate time series with missing data.
Pattern Recognit., 2019

Deep divergence-based approach to clustering.
Neural Networks, 2019

Mincut pooling in Graph Neural Networks.
CoRR, 2019

2018
Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization.
IEEE Trans. Neural Networks Learn. Syst., 2018

Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis.
IEEE Trans. Neural Networks Learn. Syst., 2018

Time series cluster kernel for learning similarities between multivariate time series with missing data.
Pattern Recognit., 2018

Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders.
CoRR, 2018

An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples.
CoRR, 2018

The deep kernelized autoencoder.
Appl. Soft Comput., 2018

Remote Sensing Image Regression for Heterogeneous Change Detection.
Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

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

Bidirectional deep-readout echo state networks.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Learning compressed representations of blood samples time series with missing data.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

On the Interpretation and Characterization of Echo State Networks Dynamics: A Complex Systems Perspective.
Proceedings of the Advances in Data Analysis with Computational Intelligence Methods, 2018

Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks.
Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics, 2018

2017
Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis
Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-70337-4, 2017

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery.
Soft Comput., 2017

Data-driven detrending of nonstationary fractal time series with echo state networks.
Inf. Sci., 2017

A novel algorithm for online inexact string matching and its FPGA implementation.
CoRR, 2017

Bidirectional deep echo state networks.
CoRR, 2017

An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting.
CoRR, 2017

Advances in Biologically Inspired Reservoir Computing.
Cogn. Comput., 2017

Training Echo State Networks with Regularization Through Dimensionality Reduction.
Cogn. Comput., 2017

A Clustering Approach to Heterogeneous Change Detection.
Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017

Spectral Clustering Using PCKID - A Probabilistic Cluster Kernel for Incomplete Data.
Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017

Deep Kernelized Autoencoders.
Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017

The time series cluster kernel.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

Deep divergence-based clustering.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

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

Critical echo state network dynamics by means of Fisher information maximization.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Temporal overdrive recurrent neural network.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016
Two density-based k-means initialization algorithms for non-metric data clustering.
Pattern Anal. Appl., 2016

Identifying user habits through data mining on call data records.
Eng. Appl. Artif. Intell., 2016

Multiplex visibility graphs to investigate recurrent neural networks dynamics.
CoRR, 2016

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data.
Cogn. Comput., 2016

2015
Prediction of telephone calls load using Echo State Network with exogenous variables.
Neural Networks, 2015

Position paper: a general framework for applying machine learning techniques in operating room.
CoRR, 2015

Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition.
IEEE Access, 2015

2014
A Granular Computing approach to the design of optimized graph classification systems.
Soft Comput., 2014

An interpretable graph-based image classifier.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

2013
Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Matching of time-varying labeled graphs.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013


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