TL;DR 👇

TL;DR 👇

I do research and all kinds of no-profit promotional activities on machine learning.

Publications

📒 Books

S. Scardapane, Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land, arXiv preprint 2404.17625, 2024.
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👐 Preprints and whitepapers

[P11] Hajij, M., et al., TopoX: A Suite of Python Packages for Machine Learning on Topological Domains, arXiv:2402.02441, 2024.
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[P10] Candelori, B., Bardella, G., Spinelli, I., Pani, P., Ferraina, S., and Scardapane, S., Spatio-temporal transformers for decoding neural movement control, bioRxiv preprint, 2024.
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[P9] Papamarkou, T., et al., Position Paper: Challenges and Opportunities in Topological Deep Learning, arXiv:2402.08871, 2024.
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[P8] Gambella, M., Pomponi, J., Scardapane, S., Roveri, M., NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks, arXiv:2401.13330, 2024.
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[P7] Strinati, E.C., et al., Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach, arXiv:2402.07573, 2024.
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[P6] Scardapane, S., Baiocchi, A., Devoto, A., Marsocci, V., Minervini, P., Pomponi, J., Conditional computation in neural networks: principles and research trends, arXiv:2403.07965, 2024.
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[P5] Pomponi, J., Devoto, A., Scardapane, S., Cascaded Scaling Classifier: class incremental learning with probability scaling, arXiv:2402.01262, 2024.
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[P4] Baiocchi, A., Spinelli, I., Nicolosi, A., Scardapane, S., Adaptive Point Transformer, arXiv:2401.14845, 2024.
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[P3] Wójcik, B., Devoto, A., Pustelnik, K., Minervini, P., Scardapane, S., Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference, arXiv:2312.10193, 2023.
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[P2] Battiloro, C., Spinelli, I., Telyatnikov, L., Bronstein, M., Scardapane, S., Di Lorenzo, P., From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module, arXiv:2305.16174, 2023.
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[P1] Telyatnikov, L., Bucarelli, M.S., Bernardez, G., Zaghen, O., Scardapane, S., Lio, P., Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design, arXiv:2310.07684, 2023.
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📰 Journal publications

[J55] Spinelli, I., Scardapane, S., & Uncini, A., A Meta-Learning Approach for Training Explainable Graph Neural Networks , IEEE Transactions on Neural Networks and Learning Systems, 35(4), pp. 4647-4655, 2024.
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[J54] Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., Hussain, A., Machine Un-learning: An Overview of Techniques, Applications, and Future Directions, Cognitive Computation, Early access, pp. 1-25, 2023.
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[J53] Ercolino, S., Devoto, A., Monorchio, L., Santini, M., Mazzaro, S., & Scardapane, S., On the robustness of vision transformers for in-flight monocular depth estimation, Industrial Artificial Intelligence, 1, pp. 1-14, 2023.
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[J52] Devoto, A., Spinelli, I., Murabito, F., Chiovoloni, F., Musmeci, R., & Scardapane, S., Re-identification of objects from aerial photos with hybrid siamese neural networks, IEEE Transactions on Industrial Informatics, 19(3), pp. 2997-3005, 2023.
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[J51] Pomponi, J., Dantoni, D., Nicolosi, A., & Scardapane, S., Rearranging pixels is a powerful black-box attack for RGB and infrared deep learning models., IEEE Access, 11, pp. 11298-11306, 2023.
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[J50] Guerra, M., Scardapane, S., Bianchi, F.M., Probabilistic load forecasting with Reservoir Computing, IEEE Access, 11, pp. 145989-146002, 2023.
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[J49] Comminiello, D., Nezamdoust, A., Scardapane, S., Scarpiniti, M., Hussain, A., & Uncini, A., A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(3), pp. 1384-1396, 2023.
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[J48] Guizzo, E., Weyde, T., Scardapane, S., & Comminiello, D., Learning Speech Emotion Representations in the Quaternion Domain, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, pp. 1200-1212, 2023.
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[J47] Pomponi, J., Scardapane, S., & Uncini, A., Continual Learning with Invertible Generative Models, Neural Networks, in press, pp. 1-10, 2023.
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[J46] Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., Hussain, A., Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence, Cognitive Computation, in press, pp. 1-30, 2023.
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[J45] Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., & Crespi, M., Inferring 3D change detection from bitemporal optical images, ISPRS Journal of Photogrammetry and Remote Sensing, 196, pp. 325-339, 2023.
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[J44] Verdone, A., Scardapane, S., Panella, M., Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production, Applied Energy, 353, Part B, pp. 1-13, 2023.
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[J43] Spinelli, I., Bianchini, R., & Scardapane, S., Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks, Neural Networks, in press, pp. 1-25, 2023.
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[J42] Maglianella, L., Nicoletti, L., Giagu, S., Napoli, C., & Scardapane, S., Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition, Computing and Software for Big Science, 428.0, pp. 1-18, 2023.
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[J41] Marsocci, V., Scardapane, S., Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, pp. 5049-5060, 2023.
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[J40] Scardapane, S., Gallicchio, C., Micheli, A., & Soriano, M.C., Guest Editorial: Trends in Reservoir Computing, Cognitive Computation, 15, pp. 1407-1408, 2023.
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[J39] Giarnieri, E., & Scardapane, S., Towards Artificial Intelligence Applications in Next Generation Cytopathology, Biomedicines, 11(8), pp. 2225, 2023.
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[J38] Lastilla, L., Ammirati, S., Firmani, D., Komodakis, N., Merialdo, P., & Scardapane, S., Self-supervised learning for medieval handwriting identification: A case study from the Vatican Apostolic Library, Information Processing & Management, 59(3), pp. 102875, 2022.
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[J37] Pomponi, J., Scardapane, S., & Uncini, A., Centroids Matching: an efficient Continual Learning approach operating in the embedding space, Transactions on Machine Learning Research, online, pp. 1-15, 2022.
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[J36] Pomponi, J., Scardapane, S., & Uncini, A., A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models, Entropy, 24(1), pp. 1-14, 2022.
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[J35] Asci, F., Scardapane, S., Zampogna, A., D'Onofrio, V., Testa, L., Patera, M., Falletti, M., Marsili, L., & Suppa, A., Handwriting Declines with Human Ageing: A Machine Learning Study, Frontiers in Aging Neuroscience, in press, pp. 1-14, 2022.
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Press release: Sapienza | Adnkronos | Repubblica | Sky TG24

[J34] Spinelli, I., Scardapane, S., Hussain, A., & Uncini, A., FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, IEEE Transactions on Artificial Intelligence, 3(3), pp. 344-354, 2022.
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[J33] 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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[J32] 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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[J31] 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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[J30] 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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[J29] Spinelli, I., Scardapane, S., & Uncini A., Adaptive Propagation Graph Convolutional Network, IEEE Transactions on Neural Networks and Learning Systems, 32(10), pp. 4755-4760, 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] 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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[J26] 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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[J25] 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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[J24] 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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[J23] 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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[J22] 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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[J21] 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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[J20] 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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[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., 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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[J17] 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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[J16] 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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[J15] 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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[J14] 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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[J13] 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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[J12] 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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[J11] 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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[J10] 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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[J9] 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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[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

[C46] Contu, R., Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., Urban 3D Change Detection with Deep Learning: Custom Data Augmentation Techniques, EGU General Assembly 2024, pp. 1, 2024 (Copernicus Meetings). Extended abstract No. EGU24-9729.
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[C45] Torda, T., Gargiulo, S., Grillo, G., Ciardiello, A., Voena, C., Giagu, S., and Scardapane, S., Tracin in Semantic Segmentation of Tumor Brains in MRI, an Extended Approach, Proceedings of the 2nd AIxIA Workshop on Artificial Intelligence For Healthcare (AI*CH), pp. 1-10, 2023 (CEUR Workshop Proceedings).
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[C44] Papillon, M., et al., ICML 2023 Topological Deep Learning Challenge : Design and Results, Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML), pp. 3-8, 2023 (PMLR 221).
Link   arXiv  

[C43] Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., & Crespi, M., New trends in urban change detection: detecting 3D changes from bitemporal optical images, EGU General Assembly 2023, pp. 1, 2023 (Copernicus Meetings). Extended abstract No. EGU23-13357.
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[C42] Marsocci, V., Gonthier, N., Garioud, A., Scardapane, S., Mallet, C., GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2075-2085, 2023 (IEEE/CVF).
Link   arXiv  

[C41] Guerra, M., Spinelli, I., Scardapane, S., & Bianchi F.M., Explainability in subgraphs-enhanced Graph Neural Networks, Proceedings of the Northern Lights Deep Learning Workshop 2023, pp. 1-7, 2023 (Septentrio Academic Publishing).
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[C40] Telyatnikov, L., & Scardapane, S., EGG-GAE: scalable graph neural networks for tabular data imputation, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1-16, 2023 (PMLR 206:2661-2676).
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[C39] Moieez, H., Marsocci, V., Scardapane, S., Continual Self-Supervised Learning in Earth Observation with Embedding Regularization, 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5029-5032, 2023 (IEEE).
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[C38] Spinelli, I., Guerra, M., Bianchi F.M., Scardapane, S., Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability, 2023 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 229-234, 2023 (ESANN).
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[C37] Farkhondeh, A., Palmero, C., Scardapane, S., & Escalera, S., Towards Self-Supervised Gaze Estimation, 2022 British Machine Vision Conference (BMVC), pp. 1-13, 2022 (The British Machine Vision Association and Society for Pattern Recognition).
Link   arXiv  

[C36] Pomponi, J., Scardapane, S., & Uncini, A., Pixle: a fast and effective black-box attack based on rearranging pixels, 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2022 (IEEE).
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[C35] Verdone, A., Scardapane, S., & Panella, M., Multi-site Forecasting of Energy Time Series with Spatio-Temporal Graph Neural Networks, 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2022 (IEEE).
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[C34] Spasiano, F., Gennaro, G., & Scardapane, S., Evaluating Adversarial Attacks and Defences in Infrared Deep Learning Monitoring Systems, 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 2022 (IEEE).
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[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] 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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[C31] 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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[C30] 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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[C29] 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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[C28] 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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[C27] 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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[C26] 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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[C25] 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).
Link   arXiv  

[C24] 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).
Link   DOI   arXiv  

[C23] 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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[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).
Link   DOI   arXiv  

[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).
Link   DOI   arXiv  

[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] 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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[C16] 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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[C15] 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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[C14] 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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[C13] 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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[C12] 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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[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

[B23] Saurio, G., Muscas, M., Spinelli, I., Rughetti, V., Della Giovampaola, I., Scardapane, S., ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo, Image Analysis and Processing - ICIAP 2023 Workshops, pp. 1-10, 2023 (Lecture Notes in Computer Science, vol 14365. Springer, Cham).
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[B22] Carraro, A., Saurio, G., López-Maestresalas, A., Scardapane, S., Marinello, F., Convolutional Neural Networks for the Detection of Esca Disease Complex in Asymptomatic Grapevine Leaves, Image Analysis and Processing - ICIAP 2023 Workshops, pp. 1-10, 2023 (Lecture Notes in Computer Science, vol 14365. Springer, Cham).
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[B21] Copur, O., Nakip, M., Scardapane, S., & Slowack Jürgen, Engagement Detection with Multi-Task Training in E-Learning Environments, Image Analysis and Processing – ICIAP 2022, pp. 411-422, 2022 (Springer).
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[B20] Gallicchio, C., & Scardapane, S., Deep randomized neural networks, Recent Trends in Learning From Data, pp. 43-68, 2020 (Springer).
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[B19] 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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[B18] 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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[B17] 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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[B16] 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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[B15] 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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[B14] 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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[B13] 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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[B12] 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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[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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