Nicolò Navarin

IEEE Trans. Neural Networks Learn. Syst., 2022

Editorial Special Issue Interaction With Artificial Intelligence Systems: New Human-Centered Perspectives and Challenges.

[BibT_eX]

[DOI]

Merylin Monaro

Emilia I. Barakova

IEEE Trans. Hum. Mach. Syst., 2022

SOM-based aggregation for graph convolutional neural networks.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2022

Polynomial-based graph convolutional neural networks for graph classification.

[BibT_eX]

[DOI]

Mach. Learn., 2022

Advances in artificial neural networks, machine learning and computational intelligence.

[BibT_eX]

[DOI]

Luca Oneto

Frank-Michael Schleif

Neurocomputing, 2022

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

[BibT_eX]

[DOI]

Neurocomputing, 2022

Advances in artificial neural networks, machine learning and computational intelligence.

[BibT_eX]

[DOI]

Luca Oneto

Kerstin Bunte

Neurocomputing, 2022

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

[BibT_eX]

[DOI]

Neurocomputing, 2022

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

[BibT_eX]

[DOI]

Comput. Hum. Behav., 2022

Compact graph neural network models for node classification.

[BibT_eX]

[DOI]

Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Face the Truth: Interpretable Emotion Genuineness Detection.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Backpropagation-free Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining, 2022

Biased Edge Dropout in NIFTY for Fair Graph Representation Learning.

[BibT_eX]

[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Deep Learning for Graphs.

[BibT_eX]

[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

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

[BibT_eX]

[DOI]

Entropy, 2021

Simple Graph Convolutional Networks.

[BibT_eX]

[DOI]

CoRR, 2021

Efficient Multilingual Deep Learning Model for Keyword Categorization.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Simple Multi-resolution Gated GNN.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Tangent Graph Convolutional Network.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Complex Data: Learning Trustworthily, Automatically, and with Guarantees.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

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

[BibT_eX]

[DOI]

Manuel Dorado-Moreno

Pedro Antonio Gutiérrez

Luis Prieto

Sancho Salcedo-Sanz

César Hervás-Martínez

Neural Networks, 2020

A framework for the definition of complex structured feature spaces.

[BibT_eX]

[DOI]

Dinh Tran-Van

Neurocomputing, 2020

Conditional Constrained Graph Variational Autoencoders for Molecule Design.

[BibT_eX]

[DOI]

Davide Rigoni

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach.

[BibT_eX]

[DOI]

Proceedings of the 29th IEEE International Conference on Robot and Human Interactive Communication, 2020

Towards Online Discovery of Data-Aware Declarative Process Models from Event Streams.

[BibT_eX]

[DOI]

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Explainable Predictive Process Monitoring.

[BibT_eX]

[DOI]

Riccardo Galanti

Bernat Coma-Puig

Massimiliano de Leoni

Josep Carmona

Proceedings of the 2nd International Conference on Process Mining, 2020

A Systematic Assessment of Deep Learning Models for Molecule Generation.

[BibT_eX]

[DOI]

Davide Rigoni

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Deep Recurrent Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Learning Deep Fair Graph Neural Networks.

[BibT_eX]

[DOI]

Luca Oneto

Michele Donini

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Linear Graph Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Learning Kernel-Based Embeddings in Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

2019

Introduction.

[BibT_eX]

[DOI]

Proceedings of the Recent Trends in Learning From Data, 2019

Universal Readout for Graph Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2019

On the definition of complex structured feature spaces.

[BibT_eX]

[DOI]

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

2018

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

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

Tree-Based Kernel for Graphs With Continuous Attributes.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity.

[BibT_eX]

[DOI]

Neural Process. Lett., 2018

Pre-training Graph Neural Networks with Kernels.

[BibT_eX]

[DOI]

CoRR, 2018

Scuba: scalable kernel-based gene prioritization.

[BibT_eX]

[DOI]

BMC Bioinform., 2018

On Filter Size in Graph Convolutional Networks.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2018

Extreme Graph Kernels for Online Learning on a Memory Budget.

[BibT_eX]

[DOI]

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

DEEP: decomposition feature enhancement procedure for graphs.

[BibT_eX]

[DOI]

Dinh Tran-Van

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Emerging trends in machine learning: beyond conventional methods and data.

[BibT_eX]

[DOI]

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

Measuring the expressivity of graph kernels through Statistical Learning Theory.

[BibT_eX]

[DOI]

Neurocomputing, 2017

An efficient graph kernel method for non-coding RNA functional prediction.

[BibT_eX]

[DOI]

Fabrizio Costa

Bioinform., 2017

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

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Deep graph node kernels: A convex approach.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Approximated Neighbours MinHash Graph Node Kernel.

[BibT_eX]

[DOI]

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

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

[BibT_eX]

[DOI]

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

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

[BibT_eX]

[DOI]

Neurocomputing, 2016

Ordered Decompositional DAG kernels enhancements.

[BibT_eX]

[DOI]

Neurocomputing, 2016

Hyper-Parameter Tuning for Graph Kernels via Multiple Kernel Learning.

[BibT_eX]

[DOI]

Carlo M. Massimo

Proceedings of the Neural Information Processing - 23rd International Conference, 2016

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.

[BibT_eX]

[DOI]

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

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

[BibT_eX]

[DOI]

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015

Multiple Graph-Kernel Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Extending Local Features with Contextual Information in Graph Kernels.

[BibT_eX]

[DOI]

Riccardo Tesselli

Proceedings of the Neural Information Processing - 22nd International Conference, 2015

Exploiting the ODD framework to define a novel effective graph kernel.

[BibT_eX]

[DOI]

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014

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

[BibT_eX]

[DOI]

PhD thesis, 2014

Graph Kernels Exploiting Weisfeiler-Lehman Graph Isomorphism Test Extensions.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 21st International Conference, 2014

2013

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

[BibT_eX]

[DOI]

Proceedings of the IJCAI 2013, 2013

2012

A Tree-Based Kernel for Graphs.

[BibT_eX]

[DOI]

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

A memory efficient graph kernel.

[BibT_eX]

[DOI]