Nicoló Navarin

发表

Generative Topographic Mapping Based Aggregation Function for GCNN

A. Sperduti, Paolo Frazzetto, Luca Pasa, 2022 .

Face the Truth: Interpretable Emotion Genuineness Detection

G. Sartori, M. Conti, C. Scarpazza, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

On Backpropagation-Free Graph Convolutions

A. Sperduti, Luca Pasa, Nicoló Navarin, 2022 .

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning

C. Alippi, Daniele Zambon, Luca Pasa, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Author Correction: Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’

E. Lavezzo, S. Toppo, A. Brazzale, 2021, Nature.

SOM-based aggregation for graph convolutional neural networks

A. Sperduti, Luca Pasa, Nicoló Navarin, 2020, Neural Computing and Applications.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’

S. Bhatt, E. Lavezzo, S. Toppo, 2020, Nature.

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview

M. Bianchini, F. Scarselli, A. Micheli, 2022, Neurocomputing.

Deep fair models for complex data: Graphs labeling and explainable face recognition

Davide Anguita, Luca Oneto, Nicolò Navarin, 2021, Neurocomputing.

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, Neural Processing Letters.

Emerging trends in machine learning: beyond conventional methods and data

Davide Anguita, Luca Oneto, Nicolò Navarin, 2018, ESANN.

A Lossy Counting Based Approach for Learning on Streams of Graphs on a Budget

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2013, IJCAI.

Scuba: scalable kernel-based gene prioritization

Giorgio Valle, Alessandro Sperduti, Fabio Aiolli, 2018, BMC Bioinformatics.

DEEP: decomposition feature enhancement procedure for graphs

Alessandro Sperduti, Nicolò Navarin, Dinh Tran-Van, 2018, ESANN.

Suppression of COVID-19 outbreak in the municipality of Vo, Italy

E. Lavezzo, S. Toppo, A. Brazzale, 2020, medRxiv.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction

Nicolò Navarin, Nicoló Navarin, 2014 .

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’

S. Bhatt, E. Lavezzo, S. Toppo, 2020, Nature.

Universal Readout for Graph Convolutional Neural Networks

Alessandro Sperduti, Nicolò Navarin, Dinh Van Tran, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Davide Anguita, Luca Oneto, Nicolò Navarin, 2021, Entropy.

Multiresolution Reservoir Graph Neural Network

A. Sperduti, Luca Pasa, Nicoló Navarin, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Polynomial-based graph convolutional neural networks for graph classification

A. Sperduti, Luca Pasa, Nicoló Navarin, 2021, Machine-mediated learning.

Detecting deception through facial expressions in a dataset of videotaped interviews: A comparison between human judges and machine learning models

G. Sartori, C. Scarpazza, M. Monaro, 2021, Comput. Hum. Behav..

Backpropagation-free Graph Neural Networks

A. Sperduti, W. Erb, Luca Pasa, 2022, 2022 IEEE International Conference on Data Mining (ICDM).

Compact graph neural network models for node classification

A. Sperduti, Luca Pasa, Nicoló Navarin, 2022, SAC.

A systematic review of value-aware recommender systems

Alvise De Biasio, F. Aiolli, Nicoló Navarin, 2023, Expert Syst. Appl..

Multi-task learning for the prediction of wind power ramp events with deep neural networks

Pedro Antonio Gutiérrez, M Dorado-Moreno, N Navarin, 2020, Neural Networks.

An Explainable Decision Support System for Predictive Process Analytics

M. Monaro, Nicolò Navarin, M. Leoni, 2022, Eng. Appl. Artif. Intell..

Explainable Predictive Process Monitoring

J. Carmona, M. Leoni, Riccardo Galanti, 2020, 2020 2nd International Conference on Process Mining (ICPM).

Idiopathic and acquired pedophilia as two distinct disorders: an insight from neuroimaging

P. Pietrini, G. Sartori, L. Finos, 2020, Brain Imaging and Behavior.

Complex Data: Learning Trustworthily, Automatically, and with Guarantees

Alessio Micheli, Franco Scarselli, Alessandro Sperduti, 2021, ESANN.

Pre-training Graph Neural Networks with Kernels pdf

Alessandro Sperduti, Nicolò Navarin, Dinh Van Tran, 2018, ArXiv.

On Filter Size in Graph Convolutional Networks

Alessandro Sperduti, Nicolò Navarin, Dinh Van Tran, 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions pdf

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2014, ICONIP.

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

Davide Anguita, Luca Oneto, Nicolò Navarin, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

An efficient graph kernel method for non‐coding RNA functional prediction

Fabrizio Costa, Nicolò Navarin, Fabrizio Costa, 2017, Bioinform..

Conditional Constrained Graph Variational Autoencoders for Molecule Design

Alessandro Sperduti, Nicolo Navarin, Davide Rigoni, 2020, 2020 IEEE Symposium Series on Computational Intelligence (SSCI).

On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity

Lamberto Ballan, L. Oneto, M. Monaro, 2023, Knowl. Based Syst..

Approximated Neighbours MinHash Graph Node Kernel

Alessandro Sperduti, Nicolò Navarin, A. Sperduti, 2017, ESANN.

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Measuring the expressivity of graph kernels through Statistical Learning Theory

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2017, Neurocomputing.

Extending local features with contextual information in graph kernels

Alessandro Sperduti, Nicolò Navarin, Riccardo Tesselli, 2015, ICONIP.

Learning Kernel-Based Embeddings in Graph Neural Networks

Alessandro Sperduti, Nicolò Navarin, Dinh Van Tran, 2020, ECAI.

Object-centric Process Predictive Analytics

M. Leoni, Riccardo Galanti, Alan Marazzi, 2022, Expert Syst. Appl..

A Tree-Based Kernel for Graphs

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2012, SDM.

Multiple Graph-Kernel Learning

Alessandro Sperduti, Fabio Aiolli, Nicolò Navarin, 2015, 2015 IEEE Symposium Series on Computational Intelligence.

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2016, ESANN.

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints

Davide Anguita, Luca Oneto, Fabio Aiolli, 2016, ESANN.

Economic Recommender Systems - A Systematic Review

Alvise De Biasio, D. Jannach, Nicoló Navarin, 2023, Electron. Commer. Res. Appl..

Topology preserving maps as aggregations for Graph Convolutional Neural Networks

A. Sperduti, Paolo Frazzetto, Luca Pasa, 2023, SAC.

SOM-based aggregation for graph convolutional neural networks

Alessandro Sperduti, Luca Pasa, Nicolò Navarin, 2020, Neural Comput. Appl..

LSTM networks for data-aware remaining time prediction of business process instances

Alessandro Sperduti, Mirko Polato, Nicolò Navarin, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Forged handwriting verification: a public domain dataset for training machine learning models

G. Sartori, M. Monaro, Valentina Fietta, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design

A. Sperduti, Nicoló Navarin, Davide Rigoni, 2023, ArXiv.

An empirical study on budget-aware online kernel algorithms for streams of graphs

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2015, Neurocomputing.

Author Correction: Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’

A. Crisanti, S. Bhatt, E. Lavezzo, 2021, Nature.

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

A. Sperduti, L. Oneto, D. Anguita, 2018, Neural Processing Letters.

Ordered Decompositional DAG kernels enhancements pdf

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2015, Neurocomputing.

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing

Vincenzo D'Amato, Danilo Franco, Luca Pasa, 2023, Neurocomputing.

Learning Deep Fair Graph Neural Networks

Luca Oneto, Nicolò Navarin, Michele Donini, 2020, ESANN.

Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach

Emanuele Menegatti, Stefano Ghidoni, Elisa Tosello, 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).

Tree-Based Kernel for Graphs With Continuous Attributes

Alessandro Sperduti, Nicolò Navarin, Giovanni Da San Martino, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Biased Edge Dropout in NIFTY for Fair Graph Representation Learning

A. Sperduti, L. Oneto, Luca Pasa, 2022, ESANN.

Deep graph node kernels: A convex approach

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Model-based approaches to profit-aware recommendation

Alvise De Biasio, Nicoló Navarin, Alvise De Biasio, 2024, Expert Syst. Appl..

Graph Representation Learning

Federico Errica, Marco Podda, Luca Pasa, 2023, ESANN.

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

L. Oneto, D. Anguita, Danilo Franco, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Empowering Simple Graph Convolutional Networks

A. Sperduti, W. Erb, Luca Pasa, 2023, IEEE Transactions on Neural Networks and Learning Systems.

Over-Parameterized Neural Models based on Graph Random Features for fast and accurate graph classification

A. Sperduti, L. Oneto, Luca Pasa, 2023 .

A Systematic Assessment of Deep Learning Models for Molecule Generation

Alessandro Sperduti, Nicolo Navarin, Davide Rigoni, 2020, ESANN.