| Recurrent Neural Networks for Short-Term Load Forecasting: An Overview and Comparative Analysis FM Bianchi, E Maiorino, MC Kampffmeyer, A Rizzi, R Jenssen Springer Briefs in Computer Science, 2017 | 524* | 2017 |
| Graph neural networks with convolutional arma filters FM Bianchi, D Grattarola, L Livi, C Alippi IEEE transactions on pattern analysis and machine intelligence 44 (7), 3496-3507, 2021 | 450 | 2021 |
| Spectral clustering with graph neural networks for graph pooling FM Bianchi, D Grattarola, C Alippi Proceedings of the 37th International Conference on Machine Learning (ICML), 2020 | 449 | 2020 |
| Reservoir computing approaches for representation and classification of multivariate time series FM Bianchi, S Scardapane, S Løkse, R Jenssen IEEE transactions on neural networks and learning systems, 2020 | 173 | 2020 |
| Short-term electric load forecasting using echo state networks and PCA decomposition FM Bianchi, E De Santis, A Rizzi, A Sadeghian IEEE Access 3, 1931-1943, 2015 | 160 | 2015 |
| Time series cluster kernel for learning similarities between multivariate time series with missing data KØ Mikalsen, FM Bianchi, C Soguero-Ruiz, R Jenssen Pattern Recognition 76, 569-581, 2018 | 120 | 2018 |
| Investigating echo-state networks dynamics by means of recurrence analysis FM Bianchi, L Livi, C Alippi IEEE transactions on neural networks and learning systems 29 (2), 427-439, 2016 | 119 | 2016 |
| Unsupervised image regression for heterogeneous change detection LT Luppino, FM Bianchi, G Moser, SN Anfinsen Transactions on Geoscience and Remote Sensing 12 (57), 9960 - 9975, 2019 | 111 | 2019 |
| Prediction of telephone calls load using echo state network with exogenous variables FM Bianchi, S Scardapane, A Uncini, A Rizzi, A Sadeghian Neural Networks 71, 204-213, 2015 | 101 | 2015 |
| Deep image translation with an affinity-based change prior for unsupervised multimodal change detection LT Luppino, M Kampffmeyer, FM Bianchi, G Moser, SB Serpico, ... IEEE Transactions on Geoscience and Remote Sensing 60, 1-22, 2021 | 95 | 2021 |
| Understanding pooling in graph neural networks D Grattarola, D Zambon, FM Bianchi, C Alippi IEEE transactions on neural networks and learning systems, 2022 | 87 | 2022 |
| Code-aligned autoencoders for unsupervised change detection in multimodal remote sensing images LT Luppino, MA Hansen, M Kampffmeyer, FM Bianchi, G Moser, ... IEEE Transactions on Neural Networks and Learning Systems, 2022 | 84 | 2022 |
| Learning representations of multivariate time series with missing data FM Bianchi, L Livi, KØ Mikalsen, M Kampffmeyer, R Jenssen Pattern Recognition 96, 106973, 2019 | 81* | 2019 |
| Training echo state networks with regularization through dimensionality reduction S Løkse, FM Bianchi, R Jenssen Cognitive Computation 9, 364-378, 2017 | 73 | 2017 |
| Determination of the edge of criticality in echo state networks through Fisher information maximization L Livi, FM Bianchi, C Alippi IEEE transactions on neural networks and learning systems 29 (3), 706-717, 2017 | 73 | 2017 |
| Large-scale detection and categorization of oil spills from SAR images with deep learning FM Bianchi, MM Espeseth, N Borch Remote Sensing 12 (14), 2260, 2020 | 68 | 2020 |
| Deep divergence-based approach to clustering M Kampffmeyer, S Løkse, FM Bianchi, L Livi, AB Salberg, R Jenssen Neural Networks 113, 91-101, 2019 | 66 | 2019 |
| Hierarchical representation learning in graph neural networks with node decimation pooling FM Bianchi, D Grattarola, L Livi, C Alippi IEEE Transactions on Neural Networks and Learning Systems 33 (5), 2195-2207, 2020 | 58 | 2020 |
| Intervention fatigue is the primary cause of strong secondary waves in the COVID-19 pandemic K Rypdal, FM Bianchi, M Rypdal International Journal of Environmental Research and Public Health, 2020 | 57 | 2020 |
| Local short term electricity load forecasting: Automatic approaches TH Dang-Ha, FM Bianchi, R Olsson 2017 international joint conference on neural networks (ijcnn), 4267-4274, 2017 | 55 | 2017 |
Robert JenssenVisual Intelligence, UiT The Arctic University of Norway & Norw. Comp. Center & P1 Centre AI, UCPH在 uit.no 的电子邮件经过验证
