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2020 – today
- 2024
[j8]Danilo Franco
, Vincenzo Stefano D'Amato, Luca Pasa, Nicolò Navarin, Luca Oneto:
Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing. Neurocomputing 563: 126948 (2024)- [j7]Giovanni Donghi, Luca Pasa, Luca Oneto, Claudio Gallicchio, Alessio Micheli, Davide Anguita, Alessandro Sperduti, Nicolò Navarin:
Investigating over-parameterized randomized graph networks. Neurocomputing 606: 128281 (2024) - [j6]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
A unified framework for backpropagation-free soft and hard gated graph neural networks. Knowl. Inf. Syst. 66(4): 2393-2416 (2024) - [j5]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Empowering Simple Graph Convolutional Networks. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4385-4399 (2024) - [c25]Giovanni Donghi, Luca Pasa, Alberto Testolin, Marco Zorzi, Alessandro Sperduti, Nicolò Navarin:
Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning. ICANN (8) 2024: 373-383 - [c24]Nicolò Navarin, Paolo Frazzetto, Luca Pasa, Pietro Verzelli, Filippo Visentin, Alessandro Sperduti, Cesare Alippi:
Physics-Informed Graph Neural Cellular Automata: an Application to Compartmental Modelling. IJCNN 2024: 1-9 - [c23]Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration. SAC 2024: 474-481 - [c22]Pier Paolo Tricomi, Luca Pajola, Luca Pasa, Mauro Conti:
"All of Me": Mining Users' Attributes from their Public Spotify Playlists. WWW (Companion Volume) 2024: 963-966 - [i5]Pier Paolo Tricomi, Luca Pajola, Luca Pasa, Mauro Conti:
"All of Me": Mining Users' Attributes from their Public Spotify Playlists. CoRR abs/2401.14296 (2024) - 2023
- [c21]Davide Bacciu, Federico Errica, Alessio Micheli, Nicolò Navarin, Luca Pasa, Marco Podda, Daniele Zambon:
Graph Representation Learning. ESANN 2023 - [c20]Nicolò Navarin, Luca Pasa, Luca Oneto, Alessandro Sperduti:
An Empirical Study of Over-Parameterized Neural Models based on Graph Random Features. ESANN 2023 - [c19]Nicolò Navarin, Luca Pasa, Claudio Gallicchio, Alessandro Sperduti:
An Untrained Neural Model for Fast and Accurate Graph Classification. ICANN (4) 2023: 278-290 - [c18]Paolo Frazzetto, Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Topology preserving maps as aggregations for Graph Convolutional Neural Networks. SAC 2023: 536-543 - 2022
- [j4]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Polynomial-based graph convolutional neural networks for graph classification. Mach. Learn. 111(4): 1205-1237 (2022) - [j3]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
SOM-based aggregation for graph convolutional neural networks. Neural Comput. Appl. 34(1): 5-24 (2022) - [j2]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Multiresolution Reservoir Graph Neural Network. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2642-2653 (2022) - [c17]Davide Bacciu, Federico Errica, Nicolò Navarin, Luca Pasa, Daniele Zambon:
Deep Learning for Graphs. ESANN 2022 - [c16]Federico Caldart, Luca Pasa, Luca Oneto, Alessandro Sperduti, Nicolò Navarin:
Biased Edge Dropout in NIFTY for Fair Graph Representation Learning. ESANN 2022 - [c15]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Backpropagation-free Graph Neural Networks. ICDM 2022: 388-397 - [c14]Matteo Munari, Luca Pasa, Daniele Zambon, Cesare Alippi, Nicolò Navarin:
Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning. IJCNN 2022: 1-8 - [c13]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Compact graph neural network models for node classification. SAC 2022: 592-599 - 2021
- [c12]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Tangent Graph Convolutional Network. ESANN 2021 - [c11]Ander Arriandiaga, Giovanni Morrone, Luca Pasa, Leonardo Badino, Chiara Bartolozzi:
Audio-Visual Target Speaker Enhancement on Multi-Talker Environment Using Event-Driven Cameras. ISCAS 2021: 1-5 - [c10]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Simple Multi-resolution Gated GNN. SSCI 2021: 1-7 - [i4]Luca Pasa, Nicolò Navarin, Wolfgang Erb, Alessandro Sperduti:
Simple Graph Convolutional Networks. CoRR abs/2106.05809 (2021) - 2020
- [c9]Nicolò Navarin, Wolfgang Erb, Luca Pasa, Alessandro Sperduti:
Linear Graph Convolutional Networks. ESANN 2020: 151-156 - [c8]Luca Pasa, Nicolò Navarin, Alessandro Sperduti:
Deep Recurrent Graph Neural Networks. ESANN 2020: 157-162 - [c7]Luca Pasa, Giovanni Morrone, Leonardo Badino:
An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-Talker Single Channel Audio-Visual ASR. ICASSP 2020: 7309-7313
2010 – 2019
- 2019
- [c6]Giovanni Morrone, Sonia Bergamaschi, Luca Pasa, Luciano Fadiga, Vadim Tikhanoff, Leonardo Badino:
Face Landmark-based Speaker-independent Audio-visual Speech Enhancement in Multi-talker Environments. ICASSP 2019: 6900-6904 - [c5]Luca Pajola, Luca Pasa, Mauro Conti:
Threat is in the Air: Machine Learning for Wireless Network Applications. WiseML@WiSec 2019: 16-21 - [i3]Luca Pasa, Giovanni Morrone, Leonardo Badino:
Joined Audio-Visual Speech Enhancement and Recognition in the Cocktail Party: The Tug Of War Between Enhancement and Recognition Losses. CoRR abs/1904.08248 (2019) - [i2]Ander Arriandiaga, Giovanni Morrone, Luca Pasa, Leonardo Badino, Chiara Bartolozzi:
Audio-Visual Target Speaker Extraction on Multi-Talker Environment using Event-Driven Cameras. CoRR abs/1912.02671 (2019) - 2018
- [i1]Giovanni Morrone, Luca Pasa, Vadim Tikhanoff, Sonia Bergamaschi, Luciano Fadiga, Leonardo Badino:
Face Landmark-based Speaker-Independent Audio-Visual Speech Enhancement in Multi-Talker Environments. CoRR abs/1811.02480 (2018) - 2017
- [c4]Luca Pasa, Alessandro Sperduti, Peter Tiño:
Linear dynamical based models for sequential domains. IJCNN 2017: 2201-2208 - 2016
- [c3]Luca Pasa, Alessandro Sperduti:
Learning Sequential Data with the Help of Linear Systems. ANNPR 2016: 3-17 - 2015
- [j1]Luca Pasa, Alberto Testolin, Alessandro Sperduti:
Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models. Neurocomputing 169: 323-333 (2015) - 2014
- [c2]Luca Pasa, Alberto Testolin, Alessandro Sperduti:
A HMM-based pre-training approach for sequential data. ESANN 2014 - [c1]Luca Pasa, Alessandro Sperduti:
Pre-training of Recurrent Neural Networks via Linear Autoencoders. NIPS 2014: 3572-3580
Coauthor Index
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last updated on 2024-10-23 20:32 CEST by the dblp team
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