A HMM-based pre-training approach for sequential data. - Luca Pasa; Alberto Testolin; Alessandro Sperduti (ESANN, 2014)

Pre-training of Recurrent Neural Networks via Linear Autoencoders. - Luca Pasa; Alessandro Sperduti (NIPS, 2014)

Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models. - Luca Pasa; Alberto Testolin; Alessandro Sperduti (Neurocomputing, 2015)

Learning Sequential Data with the Help of Linear Systems. - Luca Pasa; Alessandro Sperduti (ANNPR, 2016)

Linear dynamical based models for sequential domains. - Luca Pasa; Alessandro Sperduti; Peter Tiño (IJCNN, 2017)

Face Landmark-based Speaker-independent Audio-visual Speech Enhancement in Multi-talker Environments. - Giovanni Morrone; Sonia Bergamaschi; Luca Pasa; Luciano Fadiga; Vadim Tikhanoff; Leonardo Badino (ICASSP, 2019)

Threat is in the Air: Machine Learning for Wireless Network Applications. - Luca Pajola; Luca Pasa; Mauro Conti (WiseML@WiSec, 2019)

Joined Audio-Visual Speech Enhancement and Recognition in the Cocktail Party: The Tug Of War Between Enhancement and Recognition Losses. - Luca Pasa; Giovanni Morrone; Leonardo Badino (CoRR, 2019)

Audio-Visual Target Speaker Extraction on Multi-Talker Environment using Event-Driven Cameras. - Ander Arriandiaga; Giovanni Morrone; Luca Pasa; Leonardo Badino; Chiara Bartolozzi (CoRR, 2019)

Linear Graph Convolutional Networks. - Nicolò Navarin; Wolfgang Erb; Luca Pasa; Alessandro Sperduti (ESANN, 2020)

Deep Recurrent Graph Neural Networks. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (ESANN, 2020)

An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-Talker Single Channel Audio-Visual ASR. - Luca Pasa; Giovanni Morrone; Leonardo Badino (ICASSP, 2020)

Tangent Graph Convolutional Network. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (ESANN, 2021)

Audio-Visual Target Speaker Enhancement on Multi-Talker Environment Using Event-Driven Cameras. - Ander Arriandiaga; Giovanni Morrone; Luca Pasa; Leonardo Badino; Chiara Bartolozzi (ISCAS, 2021)

Simple Multi-resolution Gated GNN. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SSCI, 2021)

Simple Graph Convolutional Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (CoRR, 2021)

Deep Learning for Graphs. - Davide Bacciu; Federico Errica; Nicolò Navarin; Luca Pasa; Daniele Zambon (ESANN, 2022)

Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. - Federico Caldart; Luca Pasa; Luca Oneto; Alessandro Sperduti; Nicolò Navarin (ESANN, 2022)

Backpropagation-free Graph Neural Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (ICDM, 2022)

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. - Matteo Munari; Luca Pasa; Daniele Zambon; Cesare Alippi; Nicolò Navarin (IJCNN, 2022)

Compact graph neural network models for node classification. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2022)

Polynomial-based graph convolutional neural networks for graph classification. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (Mach. Learn., 2022)

SOM-based aggregation for graph convolutional neural networks. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (Neural Comput. Appl., 2022)

Multiresolution Reservoir Graph Neural Network. - Luca Pasa; Nicolò Navarin; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2022)

Graph Representation Learning. - Davide Bacciu; Federico Errica; Alessio Micheli; Nicolò Navarin; Luca Pasa; Marco Podda; Daniele Zambon (ESANN, 2023)

An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. - Nicolò Navarin; Luca Pasa; Luca Oneto; Alessandro Sperduti (ESANN, 2023)

An Untrained Neural Model for Fast and Accurate Graph Classification. - Nicolò Navarin; Luca Pasa; Claudio Gallicchio; Alessandro Sperduti (ICANN (4), 2023)

Topology preserving maps as aggregations for Graph Convolutional Neural Networks. - Paolo Frazzetto; Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2023)

Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets. - Nicolò Navarin; Luca Pasa; Alessandro Sperduti (ESANN, 2024)

Informed Machine Learning for Complex Data. - Luca Oneto; Nicolò Navarin; Alessio Micheli; Luca Pasa; Claudio Gallicchio; Davide Bacciu; Davide Anguita (ESANN, 2024)

Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning. - Giovanni Donghi; Luca Pasa; Alberto Testolin; Marco Zorzi; Alessandro Sperduti; Nicolò Navarin (ICANN (8), 2024)

Physics-Informed Graph Neural Cellular Automata: an Application to Compartmental Modelling. - Nicolò Navarin; Paolo Frazzetto; Luca Pasa; Pietro Verzelli; Filippo Visentin; Alessandro Sperduti; Cesare Alippi (IJCNN, 2024)

Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration. - Paolo Frazzetto; Luca Pasa; Nicolò Navarin; Alessandro Sperduti (SAC, 2024)

"All of Me": Mining Users' Attributes from their Public Spotify Playlists. - Pier Paolo Tricomi; Luca Pajola; Luca Pasa; Mauro Conti (WWW (Companion Volume), 2024)

Investigating over-parameterized randomized graph networks. - Giovanni Donghi; Luca Pasa; Luca Oneto; Claudio Gallicchio; Alessio Micheli; Davide Anguita; Alessandro Sperduti; Nicolò Navarin (Neurocomputing, 2024)

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. - Danilo Franco; Vincenzo Stefano D'Amato; Luca Pasa; Nicolò Navarin; Luca Oneto (Neurocomputing, 2024)

A unified framework for backpropagation-free soft and hard gated graph neural networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (Knowl. Inf. Syst., 2024)

Empowering Simple Graph Convolutional Networks. - Luca Pasa; Nicolò Navarin; Wolfgang Erb; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2024)

Moshi Moshi? A Model Selection Hijacking Adversarial Attack. - Riccardo Petrucci; Luca Pajola; Francesco Marchiori; Luca Pasa; Mauro Conti (CoRR, 2025)