Publications

👐 Preprints

[P3] Spinelli, I., Scardapane, S., Hussain, A., & Uncini, A., Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, arXiv:2104.14210, 2021.
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[P2] Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A., A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling, arXiv:2104.09641, 2021.
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[P1] Spinelli, I., Scardapane, S., & Uncini, A., A Meta-Learning Approach for Training Explainable Graph Neural Networks , arXiv:2109.09426, 2021.
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📰 Journal publications

[J34] Scardapane, S., Spinelli, I., & Di Lorenzo, P., Distributed Training of Graph Convolutional Networks, IEEE Transactions on Signal and Information Processing over Networks, 7, pp. 87-100, 2021.
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[J33] Pomponi, J., Scardapane, S., & Uncini, A., Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods, Neural Networks, In Press, pp. 1-41, 2021.
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[J32] Bianchi, F.M., Scardapane, S., Løkse, S., & Jenssen, R., Reservoir computing approaches for representation and classification of multivariate time series, IEEE Transactions on Neural Networks and Learning Systems, 32(5), pp. 2169-237X, 2021.
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[J31] Marsocci, V., Scardapane, S., & Komodakis, N., MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing, Remote Sensing, 13(16), pp. 1-17, 2021.
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[J30] Scardapane, S., Gallicchio, C., Micheli, A., & Soriano, M.C., Guest Editorial: Trends in Reservoir Computing, Cognitive Computation, early access, pp. 1-2, 2021.
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[J29] Lilli, L., Giarnieri, E., & Scardapane, S., A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells, Computational and Mathematical Methods in Medicine, 1-11, pp. 428-437, 2021.
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[J28] Pomponi, J., Scardapane, S., & Uncini, A., Bayesian Neural Networks With Maximum Mean Discrepancy Regularization, Neurocomputing, 453, pp. 428-437, 2021.
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[J27] Totaro, S., Hussain, A., & Scardapane, S., A Non-parametric Softmax for Improving Neural Attention in Time-Series Forecasting, Neurocomputing, 381, pp. 177-185, 2020.
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[J26] Spinelli, I., Scardapane, S., & Uncini, A., Missing Data Imputation with Adversarially-trained Graph Convolutional Networks, Neural Networks, 129, pp. 249-260, 2020.
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[J25] Pomponi J., Scardapane S., Lomonaco V., & Uncini A., Efficient Continual Learning in Neural Networks with Embedding Regularization, Neurocomputing, 397, pp. 139-148, 2020.
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[J24] Baccarelli, E., Scardapane, S., Scarpiniti, M., Momenzadeh, A., Uncini, A., Optimized Training and Scalable Implementation of Conditional Deep Neural Networks with Early Exits for Fog-supported IoT applications, Information Sciences, 521, pp. 107-143, 2020.
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[J23] Vecchi, R., Scardapane, S., Comminiello, D., & Uncini, A., Compressing deep quaternion neural networks with targeted regularization, CAAI Transactions on Intelligence Technology, 5(3), pp. 172-176, 2020.
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[J22] Scardapane, S., Van Vaerenbergh, S., Hussain, A., & Uncini, A., Complex-valued Neural Networks with Non-parametric Activation Functions, IEEE Transactions on Emerging Topics in Computational Intelligence, 4(2), pp. 140-150, 2020.
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[J21] Scardapane, S., Scarpiniti, M., Baccarelli, E., & Uncini, A., Why should we add early exits to neural networks?, Cognitive Computation, 12(5), pp. 954-966, 2020.
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[J20] Spinelli, I., Scardapane, S., & Uncini A., Adaptive Propagation Graph Convolutional Network, IEEE Transactions on Neural Networks and Learning Systems, Early Access, pp. 1-10, 2020.
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[J19] Scardapane, S., Van Vaerenbergh, S., & Uncini, A., Kafnets: kernel-based non-parametric activation functions for neural networks, Neural Networks, 110, pp. 4947-4956, 2019.
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[J18] Scardapane, S., & Di Lorenzo, P., Stochastic Training of Neural Networks via Successive Convex Approximations, IEEE Transactions on Neural Networks and Learning Systems, 29(10), pp. 4947-4956, 2018.
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[J17] Scardapane, S., Wang, D. & Uncini, A., Bayesian Random Vector Functional-Link Networks for Robust Data Modeling, IEEE Transactions on Cybernetics, 48(7), pp. 2049-2059, 2018.
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[J16] Scardapane, S. & Wang, D., Randomness in neural networks: an overview, WIREs Data Mining and Knowledge Discovery, 7(2), pp. 1-18, 2017.
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[J15] Scardapane, S., Comminiello, D., Hussain, A. & Uncini, A., Group Sparse Regularization for Deep Neural Networks, Neurocomputing, 241, pp. 81-89, 2017.
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[J14] Fierimonte, R., Scardapane, S., Uncini, A. & Panella, M., Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion, IEEE Transactions on Neural Networks and Learning Systems, 28(11), pp. 2699-2711, 2017.
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[J13] Scardapane, S. & Di Lorenzo, P., A Framework for Parallel and Distributed Training of Neural Networks, Neural Networks, 91, pp. 42-54, 2017.
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[J12] Scardapane, S., Butcher, J., Bianchi, F.M., & Malik, Z., Advances in Biologically Inspired Reservoir Computing [Guest Editorial], Cognitive Computation, 9(3), pp. 295-296, 2017.
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[J11] Scardapane, S., Wang, D., & Panella, M., A decentralized training algorithm for Echo State Networks in distributed big data applications, Neural Networks, 78, pp. 65-74, 2016.
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[J10] Scardapane, S., Panella, M., Comminiello, D., Hussain, A. & Uncini, A., Distributed reservoir computing with sparse readouts, IEEE Computational Intelligence Magazine, 11(4), pp. 59-70, 2016.
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[J9] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A., A Semi-supervised Random Vector Functional-Link Network based on the Transductive Framework, Information Sciences, 364-365, pp. 156–166, 2016.
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[J8] Scardapane, S. & Uncini, A., Semi-supervised Echo State Networks for Audio Classification, Cognitive Computation, 9(1), pp. 125-135, 2016.
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[J7] Bianchi, F.M., Scardapane, S., Rizzi, A., Uncini, A., & Sadeghian, A., Granular Computing Techniques for Classification and Semantic Characterization of Structured Data, Cognitive Computation, 8(3), pp. 442-461, 2016.
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[J6] Scardapane, S., Fierimonte, R, Di Lorenzo, P., Panella, M. & Uncini, A., Distributed semi-supervised support vector machines, Neural Networks, 80, pp. 43-52, 2016.
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[J5] Bianchi, F. M., Scardapane, S., Uncini, A., Rizzi, A., Sadeghian, A., Prediction of telephone calls load using Echo State Network with exogenous variables, Neural Networks, 71, pp. 204-213, 2015.
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[J4] Scardapane, S., Comminiello, D., Scarpiniti, M. & Uncini, A., Online Sequential Extreme Learning Machine With Kernels, IEEE Transactions on Neural Networks and Learning Systems, 26(9), pp. 2214-2200, 2015.
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[J3] Scardapane, S., Scarpiniti, M., Bucciarelli, M., Colone, F., Mansueto, M. V., & Parisi, R., Microphone Array Based Classification for Security Monitoring in Unstructured Environments, AEU-International Journal of Electronics and Communications, 69(11), pp. 1715-1723, 2015.
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[J2] Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R. & Uncini, A., Improving nonlinear modeling capabilities of functional link adaptive filters, Neural Networks, 69, pp. 51-59, 2015.
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[J1] Scardapane, S., Wang, D., Panella, M. & Uncini, A., Distributed Learning for Random Vector Functional-Link Networks, Information Sciences, 301, pp. 217-284, 2015.
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📅 Conference publications

[C33] Vincenzo, V., Pellegrini, L., Cossu, A., Carta, A., Graffieti, G., Hayes, T.L., De Lange, M., Masana, M., Pomponi, J., van de Ven, G., Mundt, M., She, Q., Cooper, K., Forest, J., Belouadah, E., Calderara, S., Parisi, G.I., Cuzzolin, F., Tolias, A., Scardapane, S., Antiga, L., Amhad, S., Popescu, A., Kanan, C., van de Weijer, J., Tuytelaars, T., Bacciu, D., & Maltoni, D., Avalanche: an End-to-End Library for Continual Learning, Continual Learning in Computer Vision Workshop, CVPR 2021, pp. 1-11, 2021 (CVPR).
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[C32] Falvo, A., Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A., A Wide Multimodal Dense U-Net for Fast Magnetic Resonance Imaging, Proceedings of the 20120 28th European Signal Processing Conference (EUSIPCO), pp. 1274-1278, 2020 (IEEE).
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[C31] Iurcev, M., Diviacco, P., Scardapane, S., & Muciaccia, F., Recognition of marine seismic data features using convolutional neural networks, EGU General Assembly 2020, pp. 1, 2020 (EGU).
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[C30] Celsi, M. R., Scardapane, S., & Comminiello, D., Quaternion Neural Networks for 3D Sound Source Localization in Reverberant Environments, Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1-6, 2020 (IEEE).
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[C29] Pomponi, J., Scardapane, S., & Uncini, A., Pseudo-Rehearsal for Continual Learning with Normalizing Flows, 4th Lifelong Learning Workshop, Thirty-seventh International Conference on Machine Learning, pp. 1-5, 2020 (ICML).
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[C28] Gallicchio, C., Lukoševičius‬, M., & Scardapane, S., Frontiers in Reservoir Computing, Proceedings of the 2020 European Symposium on Artificial Neural Networks (ESANN), pp. 559-566, 2020 (ESANN).
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[C27] Di Lorenzo, P., & Scardapane, S., Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation, Proceedings of the 2019 Asilomar Conference on Signals, Systems, and Computers, pp. 1-5, 2020 (IEEE).
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[C26] Scardapane, S., Comminiello, D., Scarpiniti, M., Baccarelli, E., & Uncini, A., Differentiable Branching In Deep Networks for Fast Inference, 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2020 (IEEE).
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[C25] Falvo, A., Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A., A Multimodal Dense U-Net For Accelerating Multiple Sclerosis MRI, Proceedings of the 2019 IEEE Machine Learning for Signal Processing Workshop (MLSP), pp. 1-6, 2019 (IEEE).
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[C24] Scardapane, S., Van Vaerenbergh, S., Comminiello, D., & Uncini, A., Widely Linear Kernels for Complex-Valued Kernel Activation Functions, Proceedings of the 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2019 (IEEE).
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[C23] Comminiello, D., Lella, M., Scardapane, S., & Uncini, A., Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2019 (IEEE).
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[C22] Bianchi, F. M., Scardapane, S., Løkse, S., & Jenssen, R., Bidirectional deep-readout echo state networks, Proceedings of the 2018 European Symposium on Artificial Neural Networks (ESANN), pp. 425-430, 2018 (ESANN).
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[C21] Comminiello, D., Scarpiniti, M, Scardapane, S., & Uncini, A., Sparse Functional Link Adaptive Filter Using an ℓ1-Norm Regularization, Proceedings of the 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5, 2018 (IEEE).
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[C20] Scardapane, S., Van Vaerenbergh, S., Comminiello, D., Totaro, S., & Uncini, A., Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions, Proceedings of the 2018 IEEE Machine Learning for Signal Processing Workshop (MLSP), pp. 1-6, 2018 (IEEE).
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[C19] Scardapane, S., Van Vaerenbergh, S., Comminiello, D., & Uncini, A., Improving Graph Convolutional Networks with Non-Parametric Activation Functions, Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), pp. 872-876, 2018 (IEEE).
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[C18] Comminiello, D., Scarpiniti, M., Scardapane, S., Azpicueta-Ruiz, L. A., & Uncini, A., Combined Sparse Regularization for Nonlinear Adaptive Filters, Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), pp. 336-340, 2018 (IEEE).
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[C17] Scardapane, S., Stoffl, L., Röhrbein, F. & Uncini, A., On the Use of Deep Recurrent Neural Networks for Detecting Audio Spoofing Attacks, 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3483-3490, 2017 (IEEE).
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[C16] Van Vaerenbergh, S., Scardapane, S., & Santamaria, I., Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure, 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2743-2747, 2017 (Eurasip).
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[C15] Firmani, D., Merialdo, P., Nieddu, E., & Scardapane, S., In Codice Ratio: OCR of Handwritten Latin Documents using Deep Convolutional Networks, Proceedings of the 11th International Workshop on Artificial Intelligence for Cultural Heritage (AI*CH), pp. 9-16, 2017 (CEUR Workshop Proceedings).
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[C14] Scardapane, S., Scarpiniti, M., Comminiello, D. & Uncini, A., Diffusion Spline Adaptive Filtering, 2016 24th European Signal Processing Conference (EUSIPCO), pp. 1498-1502, 2016 (Eurasip).
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[C13] Di Lorenzo, P. & Scardapane, S., Parallel and Distributed Training of Neural Networks via Successive Convex Approximation, 2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1-6, 2016 (IEEE).
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[C12] Scardapane, S., Altilio, R., Panella, M. & Uncini, A., Distributed Spectral Clustering based on Euclidean Distance Matrix Completion, 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3093-3100, 2016 (IEEE).
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[C11] Scardapane, S., Fierimonte, R., Wang, D., Panella, M. & Uncini, A., Distributed Music Classification Using Random Vector Functional-Link Nets, 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2015 (IEEE).
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[C10] Scardapane, S., Panella, M., Comminiello, D., & Uncini, A., Learning from Distributed Data Sources Using Random Vector Functional-Link Networks, Procedia Computer Science, pp. 53, 468-477, 2015 (Elsevier).
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[C9] Comminiello D., Scardapane, S., Scarpiniti, M., Parisi, R. & Uncini, A., Functional Link Expansions for Nonlinear Modeling of Audio and Speech Signals, 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2015 (IEEE).
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[C8] Bianchi, F. M., Scardapane, S., Livi, L., Uncini, A., & Rizzi, A., An interpretable graph-based image classifier, 2014 International Joint Conference on Neural Networks (IJCNN), pp. 2339-2346, 2014 (IEEE).
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[C7] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A., GP-based kernel evolution for L2-Regularization Networks, 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1674-1681, 2014 (IEEE).
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[C6] Scardapane, S., Nocco, G., Comminiello, D., Scarpiniti, M., & Uncini, A., An effective criterion for pruning reservoir’s connections in Echo State Networks, 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1205-1212, 2014 (IEEE).
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[C5] Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R., & Uncini, A., Convex combination of MIMO filters for multichannel acoustic echo cancellation, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 778-782, 2013 (IEEE).
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[C4] Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A., User-Driven Quality Enhancement for Audio Signal Processing, Audio Engineering Society Convention 134, pp. , 2013 (Audio Engineering Sociery).
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[C3] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A., Music classification using extreme learning machines, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 377-381, 2013 (IEEE).
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[C2] Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A., Interactive quality enhancement in acoustic echo cancellation, 2013 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 488-492, 2013 (IEEE).
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[C1] Alemanno, A., Travaglini, A., Scardapane, S., Comminiello, D., & Uncini, A., A Framework for Adaptive Real-Time Loudness Control, Audio Engineering Society Convention 134, pp. , 2013 (Audio Engineering Sociery).
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📚 Book publications

[B20] Scarpiniti, M., Scardapane, S., Comminiello, S., & Uncini A., Music Genre Classification Using Stacked Auto-Encoders, Neural Approaches to Dynamics of Signal Exchanges, pp. 11-19, 2020 (Springer).
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[B19] Falvo A., Comminiello D., Scardapane S., Finesi G., Scarpiniti M., & Uncini A., A Multimodal Deep Network for the Reconstruction of T2W MR Images, Progresses in Artificial Intelligence and Neural Systems, pp. 423-431, 2020 (Springer).
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[B18] Grassucci, E., Scardapane S., Comminiello, D., & Uncini A., Flexible Generative Adversarial Networks with Non-parametric Activation Functions, Progresses in Artificial Intelligence and Neural Systems, pp. 67-77, 2020 (Springer).
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[B17] Spinelli, I., Scardapane, S., Scarpiniti, M., & Uncini, A., Efficient data augmentation using graph imputation neural networks, Progresses in Artificial Intelligence and Neural Systems, pp. 43-68, 2020 (Springer).
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[B16] Gallicchio, C., & Scardapane, S., Deep randomized neural networks, Recent Trends in Learning From Data, pp. 43-68, 2020 (Springer).
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[B15] Scardapane, S., Scarpiniti, M., Comminiello, D. & Uncini, A., Learning activation functions from data using cubic spline interpolation, Neural Advances in Processing Nonlinear Dynamic Signals, pp. 73-83, 2019 (Springer).
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[B14] Scarpiniti, M., Scardapane, S., Comminiello, D., Parisi, R. & Uncini, A., Separation of Drum and Bass from Monaural Tracks, Neural Advances in Processing Nonlinear Dynamic Signals, pp. 141-151, 2019 (Springer).
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[B13] Scardapane, S., Nieddu, E., Firmani, D., & Merialdo, P., Multikernel activation functions: formulation and a case study, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp. 320-329, 2019 (Springer).
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[B12] Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R. & Uncini, A., A Low-Complexity Linear-in-the-Parameters Nonlinear Filter for Distorted Speech Signals, Neural Advances in Processing Nonlinear Dynamic Signals, pp. 107-117, 2019 (Springer).
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[B11] Scardapane, S., Chen, J., & Richard, C., Adaptation and learning over networks for nonlinear system modeling, Adaptive Learning Methods for Nonlinear System Modeling, pp. 223-242, 2018 (Elsevier).
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[B10] Scarpiniti, M., Scardapane, S., Comminiello, D., Parisi, R. & Uncini, A., Effective Blind Source Separation Based on the Adam Algorithm, Multidisciplinary Approaches to Neural Computing, pp. 57-66, 2017 (Springer).
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[B9] Scardapane, S., Altilio, R., Ciccarelli, V., Uncini, A. & Panella, M., Privacy-Preserving Data Mining for Distributed Medical Scenarios, Multidisciplinary Approaches to Neural Computing, pp. 119-128, 2017 (Springer).
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[B8] Comminiello, D., Scarpiniti, M., Scardapane, S., Parisi, R., & Uncini, A., A Nonlinear Acoustic Echo Canceller with Improved Tracking Capabilities, Recent Advances in Nonlinear Speech Processing, pp. 235-243, 2016 (Springer).
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[B7] Fierimonte, R., Scardapane, S., Panella, M., & Uncini, A., A Comparison of Consensus Strategies for Distributed Learning of Random Vector Functional-Link Networks, Advances in Neural Networks: Computational Intelligence and ICT, pp. 143-152, 2016 (Springer).
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[B6] Scardapane, S., Danilo, C., Scarpiniti, M., Parisi, R. & Uncini, A., Benchmarking Functional Link Expansions for Audio Classification Tasks, Advances in Neural Networks: Computational Intelligence and ICT, pp. 133-141, 2016 (Springer).
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[B5] Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R. & Uncini, A., Online Selection of Functional Links for Nonlinear System Identification, Advances in Neural Networks: Computational and Theoretical Issues, pp. 39-47, 2015 (Springer).
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[B4] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A., Significance-Based Pruning for Reservoir’s Neurons in Echo State Networks, Advances in Neural Networks: Computational and Theoretical Issues, pp. 31-38, 2015 (Springer).
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[B3] Scardapane, S., Comminiello, D., Scarpiniti, M., & Uncini, A., A Preliminary Study on Transductive Extreme Learning Machines, Recent Advances of Neural Network Models and Applications, pp. 25-32, 2014 (Springer).
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[B2] Scarpiniti, M., Comminiello, D., Scardapane, S., Parisi, R., & Uncini, A., Proportionate Algorithms for Blind Source Separation, Recent Advances of Neural Network Models and Applications, pp. 99-106, 2014 (Springer).
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[B1] Scardapane, S., Comminiello, D., Scarpiniti, M., Parisi, R., & Uncini, PM10 Forecasting Using Kernel Adaptive Filtering: An Italian Case Study, Neural Nets and Surroundings, pp. 93-100, 2013 (Springer).
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