Nicolò Navarin

发表

Exploiting the ODD framework to define a novel effective graph kernel

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

Emerging trends in machine learning: beyond conventional methods and data

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

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Linear Graph Convolutional Networks

Alessandro Sperduti, Luca Pasa, Nicolò Navarin, 2020, ESANN.

Extreme Graph Kernels for Online Learning on a Memory Budget

Alessandro Sperduti, Giovanni Da San Martino, Nicolò Navarin, 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

On Filter Size in Graph Convolutional Networks

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

Approximated Neighbours MinHash Graph Node Kernel

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

Deep graph node kernels: A convex approach

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

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

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

DEEP: decomposition feature enhancement procedure for graphs

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

Scuba: scalable kernel-based gene prioritization

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

Deep Recurrent Graph Neural Networks

Alessandro Sperduti, Luca Pasa, Nicolò Navarin, 2020, ESANN.

Extending local features with contextual information in graph kernels

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

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).

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

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

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity

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

Measuring the expressivity of graph kernels through Statistical Learning Theory

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

Ordered Decompositional DAG kernels enhancements pdf

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

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning

Fabrizio Costa, Ivano Lauriola, Fabio Aiolli, 2017, ESANN.

Learning Deep Fair Graph Neural Networks

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

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

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

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions pdf

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

A Tree-Based Kernel for Graphs

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

Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning

Alessandro Sperduti, Nicolò Navarin, Carlo M. Massimo, 2016, ICONIP.

Multiple Graph-Kernel Learning

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

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

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

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

Nicolò Navarin, Nicoló Navarin, 2014 .

Pre-training Graph Neural Networks with Kernels pdf

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

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

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

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).

Tangent Graph Convolutional Network

Alessandro Sperduti, Luca Pasa, Nicolò Navarin, 2021, ESANN 2021 proceedings.

Complex Data: Learning Trustworthily, Automatically, and with Guarantees

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

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

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