Publications
2021
Lanciano, Giacomo; Galli, Filippo; Cucinotta, Tommaso; Bacciu, Davide; Passarella, Andrea
Predictive Auto-scaling with OpenStack Monasca Conference Forthcoming
Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2021), Forthcoming.
BibTeX | Tags: cloud computing, pervasive computing, recurrent neural network, Sequential data
@conference{Lanciano2021,
title = { Predictive Auto-scaling with OpenStack Monasca},
author = {Giacomo Lanciano and Filippo Galli and Tommaso Cucinotta and Davide Bacciu and Andrea Passarella},
year = {2021},
date = {2021-12-06},
booktitle = {Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2021)},
keywords = {cloud computing, pervasive computing, recurrent neural network, Sequential data},
pubstate = {forthcoming},
tppubtype = {conference}
}
Cossu, Andrea; Bacciu, Davide; Carta, Antonio; Gallicchio, Claudio; Lomonaco, Vincenzo
Continual Learning with Echo State Networks Conference Forthcoming
Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)
, Forthcoming.
BibTeX | Tags: Continual learning, Echo state networks, recurrent neural network, Sequential data
@conference{Cossu2021,
title = { Continual Learning with Echo State Networks },
author = {Andrea Cossu and Davide Bacciu and Antonio Carta and Claudio Gallicchio and Vincenzo Lomonaco},
editor = {Michel Verleysen},
year = {2021},
date = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)
},
keywords = {Continual learning, Echo state networks, recurrent neural network, Sequential data},
pubstate = {forthcoming},
tppubtype = {conference}
}
Bacciu, Davide; Bianchi, Filippo Maria; Paassen, Benjamin; Alippi, Cesare
Deep learning for graphs Conference Forthcoming
Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), Forthcoming.
BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing
@conference{Bacciu2021c,
title = { Deep learning for graphs},
author = {Davide Bacciu and Filippo Maria Bianchi and Benjamin Paassen and Cesare Alippi},
editor = {Michel Verleysen},
year = {2021},
date = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)},
keywords = {deep learning, deep learning for graphs, graph data, structured data processing},
pubstate = {forthcoming},
tppubtype = {conference}
}
Dukic, Haris; Deligiorgis, Georgios; Sepe, Pierpaolo; Bacciu, Davide; Trincavelli, Marco
Inductive learning for product assortment graph completion Conference Forthcoming
Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), Forthcoming.
BibTeX | Tags: deep learning for graphs, graph data, recommendation systems
@conference{Dukic2021,
title = {Inductive learning for product assortment graph completion},
author = {Haris Dukic and Georgios Deligiorgis and Pierpaolo Sepe and Davide Bacciu and Marco Trincavelli},
editor = {Michel Verleysen},
year = {2021},
date = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)},
keywords = {deep learning for graphs, graph data, recommendation systems},
pubstate = {forthcoming},
tppubtype = {conference}
}
Valenti, Andrea; Berti, Stefano; Bacciu, Davide
Calliope - A Polyphonic Music Transformer Conference Forthcoming
Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021), Forthcoming.
BibTeX | Tags: artificial creativity, autoencoder, deep learning, generative model, music generation, transformer
@conference{Valenti2021b,
title = {Calliope - A Polyphonic Music Transformer},
author = {Andrea Valenti and Stefano Berti and Davide Bacciu},
editor = {Michel Verleysen},
year = {2021},
date = {2021-10-06},
booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2021)},
keywords = {artificial creativity, autoencoder, deep learning, generative model, music generation, transformer},
pubstate = {forthcoming},
tppubtype = {conference}
}
Schoitsch, Erwin; Mylonas, Georgios (Ed.)
Supporting Privacy Preservation by Distributed and Federated Learning on the Edge Periodical
ERCIM News, 127 , 2021.
Links | BibTeX | Tags: artificial intelligence, Continual learning, edge AI, federated learning, humanistic intelligence, reservoir computing, trustworthy AI
@periodical{Bacciu2021e,
title = {Supporting Privacy Preservation by Distributed and Federated Learning on the Edge},
author = { Davide Bacciu and Patrizio Dazzi and Alberto Gotta},
editor = {Erwin Schoitsch and Georgios Mylonas},
url = {https://ercim-news.ercim.eu/en127/r-i/supporting-privacy-preservation-by-distributed-and-federated-learning-on-the-edge},
year = {2021},
date = {2021-09-30},
issuetitle = {ERCIM News},
volume = {127},
keywords = {artificial intelligence, Continual learning, edge AI, federated learning, humanistic intelligence, reservoir computing, trustworthy AI},
pubstate = {published},
tppubtype = {periodical}
}
Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea
K-Plex Cover Pooling for Graph Neural Networks Journal Article
Data Mining and Knowledge Discovery, 2021, (Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)).
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, graph pooling, structured data processing
@article{Bacciu2021b,
title = {K-Plex Cover Pooling for Graph Neural Networks},
author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},
editor = {Annalisa Appice and Sergio Escalera and José A. Gámez and Heike Trautmann},
url = {https://link.springer.com/article/10.1007/s10618-021-00779-z, Published version},
doi = {10.1007/s10618-021-00779-z},
year = {2021},
date = {2021-09-13},
journal = {Data Mining and Knowledge Discovery},
abstract = {raph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool, builds on the concepts of graph covers and k-plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.},
note = {Accepted also as paper to the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2021)},
keywords = {deep learning, deep learning for graphs, graph data, graph pooling, structured data processing},
pubstate = {published},
tppubtype = {article}
}
raph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool, builds on the concepts of graph covers and k-plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information. Macher, G; Akarmazyan, S; Armengaud, E; Bacciu, D; Calandra, C; Danzinger, H; Dazzi, P; Davalas, C; Gennaro, De M C; Dimitriou, A; Dobaj, J; Dzambic, M; Giraudi, L; Girbal, S; Michail, D; Peroglio, R; Potenza, R; Pourdanesh, F; Seidl, M; Sardianos, C; Tserpes, K; Valtl, J; Varlamis, I; Veledar, O
Dependable Integration Concepts for Human-Centric AI-based Systems Workshop
Proceedings of the 40th International Conference on Computer Safety, Reliability and Security (SafeComp 2021), 2021, (Invited discussion paper).
BibTeX | Tags: dependable AI, humanistic intelligence, trustworthy AI
@workshop{Macher2021,
title = {Dependable Integration Concepts for Human-Centric AI-based Systems},
author = {G. Macher and S. Akarmazyan and E. Armengaud and D. Bacciu and C. Calandra and H. Danzinger and P. Dazzi and C. Davalas and M.C. De Gennaro and A. Dimitriou and J. Dobaj and M. Dzambic and L. Giraudi and S. Girbal and D. Michail and R. Peroglio and R. Potenza and F. Pourdanesh and M. Seidl and C. Sardianos and K. Tserpes and J. Valtl and I. Varlamis and O. Veledar },
year = {2021},
date = {2021-09-07},
booktitle = {Proceedings of the 40th International Conference on Computer Safety, Reliability and Security (SafeComp 2021)},
note = {Invited discussion paper},
keywords = {dependable AI, humanistic intelligence, trustworthy AI},
pubstate = {published},
tppubtype = {workshop}
}
Macher, Georg; Armengaud, Eric; Bacciu, Davide; Dobaj, Jürgen; Dzambic, Maid; Seidl, Matthias; Veledar, Omar
Dependable Integration Concepts for Human-Centric AI-based Systems Workshop
Proceedings of the 16th International Workshop on Dependable Smart Embedded Cyber-Physical Systems and Systems-of-Systems (DECSoS 2021), 2021.
Abstract | BibTeX | Tags: dependable AI, humanistic intelligence, trustworthy AI
@workshop{Macher2021b,
title = {Dependable Integration Concepts for Human-Centric AI-based Systems},
author = {Georg Macher and Eric Armengaud and Davide Bacciu and Jürgen Dobaj and Maid Dzambic and Matthias Seidl and Omar Veledar},
year = {2021},
date = {2021-09-07},
booktitle = {Proceedings of the 16th International Workshop on Dependable Smart Embedded Cyber-Physical Systems and Systems-of-Systems (DECSoS 2021)},
abstract = {The rising demand to integrate adaptive, cloud-based and/or AI-based systems is also increasing the need for associated dependability concepts. However, the practical processes and methods covering the whole life cycle still need to be instantiated. The assurance of dependability continues to be an open issue with no common solution. That is especially the case for novel AI and/or dynamical runtime-based approaches. This work focuses on engineering methods and design patterns that support the development of dependable AI-based autonomous systems. The paper presents the related body of knowledge of the TEACHING project and multiple automotive domain regulation activities and industrial working groups. It also considers the dependable architectural concepts and their impactful applicability to different scenarios to ensure the dependability of AI-based Cyber-Physical Systems of Systems (CPSoS) in the automotive domain. The paper shines the light on potential paths for dependable integration of AI-based systems into the automotive domain through identified analysis methods and targets. },
keywords = {dependable AI, humanistic intelligence, trustworthy AI},
pubstate = {published},
tppubtype = {workshop}
}
The rising demand to integrate adaptive, cloud-based and/or AI-based systems is also increasing the need for associated dependability concepts. However, the practical processes and methods covering the whole life cycle still need to be instantiated. The assurance of dependability continues to be an open issue with no common solution. That is especially the case for novel AI and/or dynamical runtime-based approaches. This work focuses on engineering methods and design patterns that support the development of dependable AI-based autonomous systems. The paper presents the related body of knowledge of the TEACHING project and multiple automotive domain regulation activities and industrial working groups. It also considers the dependable architectural concepts and their impactful applicability to different scenarios to ensure the dependability of AI-based Cyber-Physical Systems of Systems (CPSoS) in the automotive domain. The paper shines the light on potential paths for dependable integration of AI-based systems into the automotive domain through identified analysis methods and targets. Resta, Michele; Monreale, Anna; Bacciu, Davide
Occlusion-based Explanations in Deep Recurrent Models for Biomedical Signals Journal Article Forthcoming
Entropy, Forthcoming, (Special issue on Representation Learning).
Abstract | BibTeX | Tags: biomedical data, explainable AI, recurrent neural network, Sequential data
@article{Resta2021,
title = { Occlusion-based Explanations in Deep Recurrent Models for Biomedical Signals },
author = {Michele Resta and Anna Monreale and Davide Bacciu},
editor = {Fabio Aiolli and Mirko Polato},
year = {2021},
date = {2021-09-01},
journal = {Entropy},
abstract = { The biomedical field is characterized by an ever-increasing production of sequential data, which often come under the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper we propose a model agnostic explanation method, based on occlusion, enabling the learning of the input influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models which are typically used to deal with data of such nature, i.e. recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to take aware decisions. A wide experimentation on different physiological data demonstrate the effectiveness of our approach, both in classification and regression tasks. },
note = {Special issue on Representation Learning},
keywords = {biomedical data, explainable AI, recurrent neural network, Sequential data},
pubstate = {forthcoming},
tppubtype = {article}
}
The biomedical field is characterized by an ever-increasing production of sequential data, which often come under the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper we propose a model agnostic explanation method, based on occlusion, enabling the learning of the input influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models which are typically used to deal with data of such nature, i.e. recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to take aware decisions. A wide experimentation on different physiological data demonstrate the effectiveness of our approach, both in classification and regression tasks. Cossu, Andrea; Carta, Antonio; Lomonaco, Vincenzo; Bacciu, Davide
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation Journal Article Forthcoming
Neural Networks, Forthcoming.
Abstract | Links | BibTeX | Tags: Continual learning, deep learning, recurrent neural network, Sequential data
@article{Cossu2021b,
title = {Continual Learning for Recurrent Neural Networks: an Empirical Evaluation},
author = {Andrea Cossu and Antonio Carta and Vincenzo Lomonaco and Davide Bacciu},
url = {https://arxiv.org/abs/2103.07492, Arxiv},
year = {2021},
date = {2021-08-30},
journal = {Neural Networks},
abstract = { Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario. },
keywords = {Continual learning, deep learning, recurrent neural network, Sequential data},
pubstate = {forthcoming},
tppubtype = {article}
}
Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario. Bacciu, Davide; Akarmazyan, Siranush; Armengaud, Eric; Bacco, Manlio; Bravos, George; Calandra, Calogero; Carlini, Emanuele; Carta, Antonio; Cassara, Pietro; Coppola, Massimo; Davalas, Charalampos; Dazzi, Patrizio; Degennaro, Maria Carmela; Sarli, Daniele Di; Dobaj, Jürgen; Gallicchio, Claudio; Girbal, Sylvain; Gotta, Alberto; Groppo, Riccardo; Lomonaco, Vincenzo; Macher, Georg; Mazzei, Daniele; Mencagli, Gabriele; Michail, Dimitrios; Micheli, Alessio; Peroglio, Roberta; Petroni, Salvatore; Potenza, Rosaria; Pourdanesh, Farank; Sardianos, Christos; Tserpes, Konstantinos; Tagliabò, Fulvio; Valtl, Jakob; Varlamis, Iraklis; Veledar, Omar (Ed.)
TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence Conference Forthcoming
Proceedings of the 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) , Forthcoming.
BibTeX | Tags: artificial intelligence, Continual learning, federated learning, humanistic intelligence, reservoir computing, trustworthy AI
@conference{Bacciu2021d,
title = {TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence},
editor = {Davide Bacciu and Siranush Akarmazyan and Eric Armengaud and Manlio Bacco and George Bravos and Calogero Calandra and Emanuele Carlini and Antonio Carta and Pietro Cassara and Massimo Coppola and Charalampos Davalas and Patrizio Dazzi and Maria Carmela Degennaro and Daniele Di Sarli and Jürgen Dobaj and Claudio Gallicchio and Sylvain Girbal and Alberto Gotta and Riccardo Groppo and Vincenzo Lomonaco and Georg Macher and Daniele Mazzei and Gabriele Mencagli and Dimitrios Michail and Alessio Micheli and Roberta Peroglio and Salvatore Petroni and Rosaria Potenza and Farank Pourdanesh and Christos Sardianos and Konstantinos Tserpes and Fulvio Tagliabò and Jakob Valtl and Iraklis Varlamis and Omar Veledar},
year = {2021},
date = {2021-08-23},
booktitle = {Proceedings of the 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) },
keywords = {artificial intelligence, Continual learning, federated learning, humanistic intelligence, reservoir computing, trustworthy AI},
pubstate = {forthcoming},
tppubtype = {conference}
}
Andrea Rosasco Antonio Carta, Andrea Cossu Vincenzo Lomonaco Davide Bacciu
Distilled Replay: Overcoming Forgetting through Synthetic Samples Workshop Forthcoming
IJCAI 2021 workshop on continual semi-supervised learning (CSSL 2021) , Forthcoming.
Links | BibTeX | Tags: Continual learning, dataset distillation, deep learning
@workshop{Rosasco2021,
title = {Distilled Replay: Overcoming Forgetting through Synthetic Samples},
author = {Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu},
url = {https://arxiv.org/abs/2103.15851, Arxiv},
year = {2021},
date = {2021-08-19},
booktitle = {IJCAI 2021 workshop on continual semi-supervised learning (CSSL 2021) },
keywords = {Continual learning, dataset distillation, deep learning},
pubstate = {forthcoming},
tppubtype = {workshop}
}
Atzeni, Daniele; Bacciu, Davide; Errica, Federico; Micheli, Alessio
Modeling Edge Features with Deep Bayesian Graph Networks Conference Forthcoming
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.
BibTeX | Tags: deep learning for graphs, generative model, hidden Markov models, structured data processing
@conference{Atzeni2021,
title = { Modeling Edge Features with Deep Bayesian Graph Networks},
author = {Daniele Atzeni and Davide Bacciu and Federico Errica and Alessio Micheli},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
organization = {IEEE},
keywords = {deep learning for graphs, generative model, hidden Markov models, structured data processing},
pubstate = {forthcoming},
tppubtype = {conference}
}
Numeroso, Danilo; Bacciu, Davide
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks Conference Forthcoming
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.
BibTeX | Tags: deep learning for graphs, explainable AI, graph data, structured data processing
@conference{Numeroso2021,
title = {MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks},
author = {Danilo Numeroso and Davide Bacciu},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
organization = {IEEE},
keywords = {deep learning for graphs, explainable AI, graph data, structured data processing},
pubstate = {forthcoming},
tppubtype = {conference}
}
Bacciu, Davide; Sarli, Daniele Di; Faraji, Pouria; Gallicchio, Claudio; Micheli, Alessio
Federated Reservoir Computing Neural Networks Conference Forthcoming
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE, Forthcoming.
BibTeX | Tags: activity recognition, distributed learning, Echo state networks, federated learning, internet of things, pervasive computing, randomized networks, reservoir computing, Sequential data
@conference{BacciuIJCNN2021,
title = {Federated Reservoir Computing Neural Networks},
author = {Davide Bacciu and Daniele Di Sarli and Pouria Faraji and Claudio Gallicchio and Alessio Micheli},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
publisher = {IEEE},
keywords = {activity recognition, distributed learning, Echo state networks, federated learning, internet of things, pervasive computing, randomized networks, reservoir computing, Sequential data},
pubstate = {forthcoming},
tppubtype = {conference}
}
Bacciu, Davide; Podda, Marco
GraphGen-Redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation Conference Forthcoming
Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE Forthcoming.
BibTeX | Tags: deep learning, deep learning for graphs, generative model, graph data
@conference{BacciuPoddaIJCNN2021,
title = {GraphGen-Redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation},
author = {Davide Bacciu and Marco Podda},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021)},
organization = {IEEE},
keywords = {deep learning, deep learning for graphs, generative model, graph data},
pubstate = {forthcoming},
tppubtype = {conference}
}
Errica, Federico; Bacciu, Davide; Micheli, Alessio
Graph Mixture Density Networks Conference Forthcoming
Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Forthcoming.
BibTeX | Tags: deep learning for graphs, generative model, graph data, structured data processing
@conference{Errica2021,
title = {Graph Mixture Density Networks},
author = {Federico Errica and Davide Bacciu and Alessio Micheli},
year = {2021},
date = {2021-07-18},
booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML 2021)},
keywords = {deep learning for graphs, generative model, graph data, structured data processing},
pubstate = {forthcoming},
tppubtype = {conference}
}
Lomonaco, Vincenzo; Pellegrini, Lorenzo; Cossu, Andrea; Carta, Antonio; Graffieti, Gabriele; Hayes, Tyler L; Lange, Matthias De; Masana, Marc; Pomponi, Jary; van de Ven, Gido; Mundt, Martin; She, Qi; Cooper, Keiland; Forest, Jeremy; Belouadah, Eden; Calderara, Simone; Parisi, German I; Cuzzolin, Fabio; Tolias, Andreas; Scardapane, Simone; Antiga, Luca; Amhad, Subutai; Popescu, Adrian; Kanan, Christopher; van de Weijer, Joost; Tuytelaars, Tinne; Bacciu, Davide; Maltoni, Davide
Avalanche: an End-to-End Library for Continual Learning Workshop Forthcoming
Proceedings of the CVPR 2021 Workshop on Continual Learning , Forthcoming.
Links | BibTeX | Tags: Continual learning, deep learning, software
@workshop{lomonaco2021avalanche,
title = {Avalanche: an End-to-End Library for Continual Learning},
author = {Vincenzo Lomonaco and Lorenzo Pellegrini and Andrea Cossu and Antonio Carta and Gabriele Graffieti and Tyler L Hayes and Matthias De Lange and Marc Masana and Jary Pomponi and Gido van de Ven and Martin Mundt and Qi She and Keiland Cooper and Jeremy Forest and Eden Belouadah and Simone Calderara and German I Parisi and Fabio Cuzzolin and Andreas Tolias and Simone Scardapane and Luca Antiga and Subutai Amhad and Adrian Popescu and Christopher Kanan and Joost van de Weijer and Tinne Tuytelaars and Davide Bacciu and Davide Maltoni},
url = {https://arxiv.org/abs/2104.00405, Arxiv},
year = {2021},
date = {2021-06-19},
booktitle = {Proceedings of the CVPR 2021 Workshop on Continual Learning },
keywords = {Continual learning, deep learning, software},
pubstate = {forthcoming},
tppubtype = {workshop}
}
Sattar, Asma; Bacciu, Davide
Context-aware Graph Convolutional Autoencoder Conference Forthcoming
Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021), Forthcoming.
BibTeX | Tags: deep learning for graphs, graph data, recommendation systems
@conference{Sattar2021,
title = {Context-aware Graph Convolutional Autoencoder},
author = {Asma Sattar and Davide Bacciu
},
year = {2021},
date = {2021-06-16},
booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},
keywords = {deep learning for graphs, graph data, recommendation systems},
pubstate = {forthcoming},
tppubtype = {conference}
}
Bacciu, Davide; Sarli, Daniele Di; Gallicchio, Claudio; Micheli, Alessio; Puccinelli, Niccolo
Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition Conference Forthcoming
Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021), Forthcoming.
BibTeX | Tags: activity recognition, Echo state networks, recurrent neural network, reservoir computing
@conference{Bacciu2021,
title = {Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition},
author = {Davide Bacciu and Daniele Di Sarli and Claudio Gallicchio and Alessio Micheli and Niccolo Puccinelli},
year = {2021},
date = {2021-06-16},
booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},
keywords = {activity recognition, Echo state networks, recurrent neural network, reservoir computing},
pubstate = {forthcoming},
tppubtype = {conference}
}
Castellana, Daniele; Bacciu, Davide
A Tensor Framework for Learning in Structured Domains Journal Article Forthcoming
Neurocomputing, Forthcoming.
BibTeX | Tags: deep learning, structured data processing, tensor factorization, tensor neural networks, tree structured data
@article{Castellana2021,
title = {A Tensor Framework for Learning in Structured Domains},
author = {Daniele Castellana and Davide Bacciu},
editor = {Kerstin Bunte and Niccolo Navarin and Luca Oneto},
year = {2021},
date = {2021-05-03},
journal = {Neurocomputing},
keywords = {deep learning, structured data processing, tensor factorization, tensor neural networks, tree structured data},
pubstate = {forthcoming},
tppubtype = {article}
}
Carta, Antonio; Sperduti, Alessandro; Bacciu, Davide
Encoding-based Memory for Recurrent Neural Networks Journal Article
Neurocomputing, 2021.
Abstract | Links | BibTeX | Tags: autoencoder, deep learning, memory networks, recurrent neural network, Sequential data
@article{Carta2021b,
title = {Encoding-based Memory for Recurrent Neural Networks},
author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu},
url = {https://arxiv.org/abs/2001.11771, Arxiv},
doi = {10.1016/j.neucom.2021.04.051},
year = {2021},
date = {2021-04-20},
journal = {Neurocomputing},
abstract = {Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study the memorization subtask from the point of view of the design and training of recurrent neural networks. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. We extend the memorization component with a modular memory that encodes the hidden state sequence at different sampling frequencies. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. The experimental results on synthetic and real-world datasets show that specializing the training algorithm to train the memorization component always improves the final performance whenever the memorization of long sequences is necessary to solve the problem. },
keywords = {autoencoder, deep learning, memory networks, recurrent neural network, Sequential data},
pubstate = {published},
tppubtype = {article}
}
Learning to solve sequential tasks with recurrent models requires the ability to memorize long sequences and to extract task-relevant features from them. In this paper, we study the memorization subtask from the point of view of the design and training of recurrent neural networks. We propose a new model, the Linear Memory Network, which features an encoding-based memorization component built with a linear autoencoder for sequences. We extend the memorization component with a modular memory that encodes the hidden state sequence at different sampling frequencies. Additionally, we provide a specialized training algorithm that initializes the memory to efficiently encode the hidden activations of the network. The experimental results on synthetic and real-world datasets show that specializing the training algorithm to train the memorization component always improves the final performance whenever the memorization of long sequences is necessary to solve the problem. Carta, Antonio; Cossu, Andrea; Errica, Federico; Bacciu, Davide
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification Workshop
The Web Conference 2021 Workshop on Graph Learning Benchmarks (GLB21), 2021.
Abstract | BibTeX | Tags: Continual learning, deep learning for graphs, structured data processing
@workshop{Carta2021,
title = { Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification },
author = {Antonio Carta and Andrea Cossu and Federico Errica and Davide Bacciu},
year = {2021},
date = {2021-04-12},
booktitle = {The Web Conference 2021 Workshop on Graph Learning Benchmarks (GLB21)},
abstract = {In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments.},
keywords = {Continual learning, deep learning for graphs, structured data processing},
pubstate = {published},
tppubtype = {workshop}
}
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a tangible impact on performances when applied to graph data. To do so, we experiment with a structure-agnostic model and a deep graph network in a robust and controlled environment on three different datasets. The benchmark is complemented by an investigation on the effect of structure-preserving regularization techniques on catastrophic forgetting. We find that replay is the most effective strategy in so far, which also benefits the most from the use of regularization. Our findings suggest interesting future research at the intersection of the continual and graph representation learning fields. Finally, we provide researchers with a flexible software framework to reproduce our results and carry out further experiments. Errica, Federico; Giulini, Marco; Bacciu, Davide; Menichetti, Roberto; Micheli, Alessio; Potestio, Raffaello
A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins Journal Article
Frontiers in Molecular Biosciences, 8 , pp. 136, 2021.
Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing
@article{errica_deep_2021,
title = {A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins},
author = {Federico Errica and Marco Giulini and Davide Bacciu and Roberto Menichetti and Alessio Micheli and Raffaello Potestio},
doi = {10.3389/fmolb.2021.637396},
year = {2021},
date = {2021-02-28},
journal = {Frontiers in Molecular Biosciences},
volume = {8},
pages = {136},
publisher = {Frontiers},
keywords = {deep learning, deep learning for graphs, graph data, structured data processing},
pubstate = {published},
tppubtype = {article}
}
Bontempi, Gianluca; Chavarriaga, Ricardo; Canck, Hans De; Girardi, Emanuela; Hoos, Holger; Kilbane-Dawe, Iarla; Ball, Tonio; Nowé, Ann; Sousa, Jose; Bacciu, Davide; Aldinucci, Marco; Domenico, Manlio De; Saffiotti, Alessandro; Maratea, Marco
The CLAIRE COVID-19 initiative: approach, experiences and recommendations Journal Article
Ethics and Information Technology, 2021.
Links | BibTeX | Tags: artificial intelligence, bioinformatics, biomedical data
@article{Bontempi2021,
title = {The CLAIRE COVID-19 initiative: approach, experiences and recommendations},
author = {Gianluca Bontempi and Ricardo Chavarriaga and Hans De Canck and Emanuela Girardi and Holger Hoos and Iarla Kilbane-Dawe and Tonio Ball and Ann Nowé and Jose Sousa and Davide Bacciu and Marco Aldinucci and Manlio De Domenico and Alessandro Saffiotti and Marco Maratea},
doi = {10.1007/s10676-020-09567-7},
year = {2021},
date = {2021-02-09},
journal = {Ethics and Information Technology},
keywords = {artificial intelligence, bioinformatics, biomedical data},
pubstate = {published},
tppubtype = {article}
}
Andrea Valenti Michele Barsotti, Davide Bacciu ; Ascari, Luca
A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting Journal Article
Bioengineering , 2021.
Links | BibTeX | Tags: autoencoder, biomedical data, deep learning, Sequential data
@article{Valenti2021,
title = {A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting},
author = {Andrea Valenti, Michele Barsotti, Davide Bacciu and Luca Ascari
},
url = {https://www.mdpi.com/2306-5354/8/2/21, Open Access },
doi = {10.3390/bioengineering8020021},
year = {2021},
date = {2021-02-05},
journal = {Bioengineering },
keywords = {autoencoder, biomedical data, deep learning, Sequential data},
pubstate = {published},
tppubtype = {article}
}
Bacciu, Davide; Bertoncini, Gioele; Morelli, Davide
Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data Journal Article
Neural Computing Applications, 2021.
Links | BibTeX | Tags: biomedical data, data visualization, explainable AI, internet of things, multivariate time-series, self-organizing map
@article{BacciuNCA2020,
title = {Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data},
author = {Davide Bacciu and Gioele Bertoncini and Davide Morelli},
doi = {10.1007/s00521-020-05600-4},
year = {2021},
date = {2021-01-04},
journal = {Neural Computing Applications},
keywords = {biomedical data, data visualization, explainable AI, internet of things, multivariate time-series, self-organizing map},
pubstate = {published},
tppubtype = {article}
}
2020
Ronchetti, Matteo; Bacciu, Davide
Generative Tomography Reconstruction Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Deep Learning and Inverse Problems, 2020.
Abstract | Links | BibTeX | Tags: adversarial learning, biomedical data, deep learning, generative model, inverse problems, machine vision
@workshop{tomographyNeurips2020,
title = {Generative Tomography Reconstruction},
author = {Matteo Ronchetti and Davide Bacciu},
url = {https://arxiv.org/pdf/2010.14933.pdf, PDF},
year = {2020},
date = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Deep Learning and Inverse Problems},
abstract = {We propose an end-to-end differentiable architecture for tomography reconstruc-1tion that directly maps a noisy sinogram into a denoised reconstruction. Compared2to existing approaches our end-to-end architecture produces more accurate recon-3structions while using less parameters and time. We also propose a generative4model that, given a noisy sinogram, can sample realistic reconstructions. This5generative model can be used as prior inside an iterative process that, by tak-6ing into consideration the physical model, can reduce artifacts and errors in the7reconstructions.},
keywords = {adversarial learning, biomedical data, deep learning, generative model, inverse problems, machine vision},
pubstate = {published},
tppubtype = {workshop}
}
We propose an end-to-end differentiable architecture for tomography reconstruc-1tion that directly maps a noisy sinogram into a denoised reconstruction. Compared2to existing approaches our end-to-end architecture produces more accurate recon-3structions while using less parameters and time. We also propose a generative4model that, given a noisy sinogram, can sample realistic reconstructions. This5generative model can be used as prior inside an iterative process that, by tak-6ing into consideration the physical model, can reduce artifacts and errors in the7reconstructions. Bacciu, Davide; Conte, Alessio; Grossi, Roberto; Landolfi, Francesco; Marino, Andrea
K-plex Cover Pooling for Graph Neural Networks Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020.
Abstract | BibTeX | Tags: deep learning, deep learning for graphs, graph data, graph pooling, structured data processing
@workshop{kplexWS2020,
title = {K-plex Cover Pooling for Graph Neural Networks},
author = {Davide Bacciu and Alessio Conte and Roberto Grossi and Francesco Landolfi and Andrea Marino},
year = {2020},
date = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms},
abstract = {We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links. The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs. },
keywords = {deep learning, deep learning for graphs, graph data, graph pooling, structured data processing},
pubstate = {published},
tppubtype = {workshop}
}
We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern. Our pooling method, named KPlexPool, builds on the concepts of graph covers and $k$-plexes, i.e. pseudo-cliques where each node can miss up to $k$ links. The experimental evaluation on molecular and social graph classification shows that KPlexPool achieves state of the art performances, supporting the intuition that well-founded graph-theoretic approaches can be effectively integrated in learning models for graphs. Bacciu, Davide; Numeroso, Danilo
Explaining Deep Graph Networks with Molecular Counterfactuals Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020.
Abstract | Links | BibTeX | Tags: deep learning for graphs, explainable AI, graph data, structured data processing
@workshop{megWS2020,
title = {Explaining Deep Graph Networks with Molecular Counterfactuals},
author = {Davide Bacciu and Danilo Numeroso},
url = {https://arxiv.org/pdf/2011.05134.pdf, Arxiv},
year = {2020},
date = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral)},
abstract = {We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator). We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties. We discuss preliminary results showing how the model can convey non-ML experts with key insights into the learning model focus in the neighborhood of a molecule. },
keywords = {deep learning for graphs, explainable AI, graph data, structured data processing},
pubstate = {published},
tppubtype = {workshop}
}
We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator). We generate informative counterfactual explanations for a specific prediction under the form of (valid) compounds with high structural similarity and different predicted properties. We discuss preliminary results showing how the model can convey non-ML experts with key insights into the learning model focus in the neighborhood of a molecule. Carta, Antonio; Sperduti, Alessandro; Bacciu, Davide
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization Workshop
34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Beyond BackPropagation: Novel Ideas for Training Neural Architectures, 2020.
Abstract | Links | BibTeX | Tags: deep learning, memory networks, recurrent neural network, Sequential data
@workshop{CartaNeuripsWS2020,
title = { Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization },
author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu
},
url = {https://arxiv.org/abs/2011.02886, Arxiv},
year = {2020},
date = {2020-12-11},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Beyond BackPropagation: Novel Ideas for Training Neural Architectures},
abstract = {Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We introduce an initialization schema that pretrains the weights of a recurrent neural network to approximate the linear autoencoder of the input sequences and we show how such pretraining can better support solving hard classification tasks with long sequences. We test our approach on sequential and permuted MNIST. We show that the proposed approach achieves a much lower reconstruction error for long sequences and a better gradient propagation during the finetuning phase. },
keywords = {deep learning, memory networks, recurrent neural network, Sequential data},
pubstate = {published},
tppubtype = {workshop}
}
Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We introduce an initialization schema that pretrains the weights of a recurrent neural network to approximate the linear autoencoder of the input sequences and we show how such pretraining can better support solving hard classification tasks with long sequences. We test our approach on sequential and permuted MNIST. We show that the proposed approach achieves a much lower reconstruction error for long sequences and a better gradient propagation during the finetuning phase. Castellana, Daniele; Bacciu, Davide
Learning from Non-Binary Constituency Trees via Tensor Decomposition Conference
PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020), 2020.
BibTeX | Tags: natural language processing, tensor factorization, tensor neural networks, tree structured data
@conference{CastellanaCOLING2020,
title = {Learning from Non-Binary Constituency Trees via Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
year = {2020},
date = {2020-12-08},
booktitle = {PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020)},
keywords = {natural language processing, tensor factorization, tensor neural networks, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
Valenti, Andrea; Barsotti, Michele; Brondi, Raffaello; Bacciu, Davide; Ascari, Luca
Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2020.
Abstract | Links | BibTeX | Tags: autoencoder, biomedical data, deep learning, Sequential data
@conference{smc2020,
title = {ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs},
author = {Andrea Valenti and Michele Barsotti and Raffaello Brondi and Davide Bacciu and Luca Ascari},
url = {https://arxiv.org/abs/2008.13485, Arxiv},
year = {2020},
date = {2020-10-11},
booktitle = {Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
publisher = {IEEE},
abstract = { Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way. Deep learning algorithms are capable of learning flexible nonlinear functions directly from data, and their constant processing latency is perfect for their deployment into online BCI systems. However, it is crucial for the jitter of the processing system to be as low as possible, in order to avoid unpredictable behaviour that can ruin the system's overall usability. In this paper, we present a novel encoding method, based on on deep convolutional autoencoders, that is able to perform efficient compression of the raw EEG inputs. We deploy our model in a ROS-Neuro node, thus making it suitable for the integration in ROS-based BCI and robotic systems in real world scenarios. The experimental results show that our system is capable to generate meaningful compressed encoding preserving to original information contained in the raw input. They also show that the ROS-Neuro node is able to produce such encodings at a steady rate, with minimal jitter. We believe that our system can represent an important step towards the development of an effective BCI processing pipeline fully standardized in ROS-Neuro framework. },
keywords = {autoencoder, biomedical data, deep learning, Sequential data},
pubstate = {published},
tppubtype = {conference}
}
Typical EEG-based BCI applications require the computation of complex functions over the noisy EEG channels to be carried out in an efficient way. Deep learning algorithms are capable of learning flexible nonlinear functions directly from data, and their constant processing latency is perfect for their deployment into online BCI systems. However, it is crucial for the jitter of the processing system to be as low as possible, in order to avoid unpredictable behaviour that can ruin the system's overall usability. In this paper, we present a novel encoding method, based on on deep convolutional autoencoders, that is able to perform efficient compression of the raw EEG inputs. We deploy our model in a ROS-Neuro node, thus making it suitable for the integration in ROS-based BCI and robotic systems in real world scenarios. The experimental results show that our system is capable to generate meaningful compressed encoding preserving to original information contained in the raw input. They also show that the ROS-Neuro node is able to produce such encodings at a steady rate, with minimal jitter. We believe that our system can represent an important step towards the development of an effective BCI processing pipeline fully standardized in ROS-Neuro framework. Bacciu, Davide; Errica, Federico; Micheli, Alessio; Podda, Marco
A Gentle Introduction to Deep Learning for Graphs Journal Article
Neural Networks, 129 , pp. 203-221, 2020.
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing
@article{gentleGraphs2020,
title = {A Gentle Introduction to Deep Learning for Graphs},
author = {Davide Bacciu and Federico Errica and Alessio Micheli and Marco Podda},
url = {https://arxiv.org/abs/1912.12693, Arxiv
https://doi.org/10.1016/j.neunet.2020.06.006, Original Paper},
doi = {10.1016/j.neunet.2020.06.006},
year = {2020},
date = {2020-09-01},
journal = {Neural Networks},
volume = {129},
pages = {203-221},
publisher = {Elsevier},
abstract = {The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field. },
keywords = {deep learning, deep learning for graphs, graph data, structured data processing},
pubstate = {published},
tppubtype = {article}
}
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field. Bacciu, Davide; Errica, Federico; Micheli, Alessio
Probabilistic Learning on Graphs via Contextual Architectures Journal Article
Journal of Machine Learning Research, 21 (134), pp. 1−39, 2020.
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing
@article{jmlrCGMM20,
title = {Probabilistic Learning on Graphs via Contextual Architectures},
author = {Davide Bacciu and Federico Errica and Alessio Micheli},
editor = {Pushmeet Kohli},
url = {http://jmlr.org/papers/v21/19-470.html, Paper},
year = {2020},
date = {2020-07-27},
journal = {Journal of Machine Learning Research},
volume = {21},
number = {134},
pages = {1−39},
abstract = {We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighborhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices.
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature.},
keywords = {deep learning, deep learning for graphs, graph data, hidden tree Markov model, structured data processing},
pubstate = {published},
tppubtype = {article}
}
We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighborhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural networks, with benefits in terms of context spreading between vertices.
First, the model learns from graphs via maximum likelihood estimation without using target labels.
Then, a supervised readout is applied to the learned graph embeddings to deal with graph classification and vertex classification tasks, showing competitive results against neural models for graphs. The computational complexity is linear in the number of edges, facilitating learning on large scale data sets. By studying how depth affects the performances of our model, we discover that a broader context generally improves performances. In turn, this leads to a critical analysis of some benchmarks used in literature. Castellana, Daniele; Bacciu, Davide
Generalising Recursive Neural Models by Tensor Decomposition Conference
Proceedings of the 2020 IEEE World Congress on Computational Intelligence, 2020.
Links | BibTeX | Tags: deep learning, structured data processing, tensor factorization, tensor neural networks, tree structured data
@conference{Wcci20Tensor,
title = {Generalising Recursive Neural Models by Tensor Decomposition},
author = {Daniele Castellana and Davide Bacciu},
url = {https://arxiv.org/abs/2006.10021, Arxiv},
year = {2020},
date = {2020-07-19},
booktitle = {Proceedings of the 2020 IEEE World Congress on Computational Intelligence},
keywords = {deep learning, structured data processing, tensor factorization, tensor neural networks, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
Cossu, Andrea; Carta, Antonio; Bacciu, Davide
Continual Learning with Gated Incremental Memories for Sequential Data Processing Conference
Proceedings of the 2020 IEEE World Congress on Computational Intelligence, 2020.
Links | BibTeX | Tags: Continual learning, deep learning, recurrent neural network, Sequential data
@conference{Wcci20CL,
title = {Continual Learning with Gated Incremental Memories for Sequential Data Processing},
author = {Andrea Cossu and Antonio Carta and Davide Bacciu},
url = {https://arxiv.org/pdf/2004.04077.pdf, Arxiv},
doi = {10.1109/IJCNN48605.2020.9207550},
year = {2020},
date = {2020-07-19},
booktitle = {Proceedings of the 2020 IEEE World Congress on Computational Intelligence},
keywords = {Continual learning, deep learning, recurrent neural network, Sequential data},
pubstate = {published},
tppubtype = {conference}
}
Valenti, Andrea; Carta, Antonio; Bacciu, Davide
Learning a Latent Space of Style-Aware Music Representations by Adversarial Autoencoders Conference
Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), 2020.
Links | BibTeX | Tags: artificial creativity, autoencoder, deep learning, generative model, music generation
@conference{ecai2020,
title = { Learning a Latent Space of Style-Aware Music Representations by Adversarial Autoencoders},
author = {Andrea Valenti and Antonio Carta and Davide Bacciu},
url = {https://arxiv.org/abs/2001.05494},
year = {2020},
date = {2020-06-08},
booktitle = {Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020)},
keywords = {artificial creativity, autoencoder, deep learning, generative model, music generation},
pubstate = {published},
tppubtype = {conference}
}
Carta, Antonio; Sperduti, Alessandro; Bacciu, Davide
Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory Conference
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020), Springer International Publishing, 2020.
Abstract | BibTeX | Tags: autoencoder, deep learning, memory networks, recurrent neural network
@conference{ecml2020LMN,
title = {Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory},
author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu},
year = {2020},
date = {2020-06-05},
booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)},
publisher = {Springer International Publishing},
abstract = {The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.},
keywords = {autoencoder, deep learning, memory networks, recurrent neural network},
pubstate = {published},
tppubtype = {conference}
}
The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.
Podda, Marco; Bacciu, Davide; Micheli, Alessio
A Deep Generative Model for Fragment-Based Molecule Generation Conference
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) , 2020.
Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing
@conference{aistats2020,
title = {A Deep Generative Model for Fragment-Based Molecule Generation},
author = {Marco Podda and Davide Bacciu and Alessio Micheli},
url = {https://arxiv.org/abs/2002.12826},
year = {2020},
date = {2020-06-03},
booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) },
abstract = {Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learn their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid or duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based clustering strategy that helps to generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision.},
keywords = {deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing},
pubstate = {published},
tppubtype = {conference}
}
Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learn their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid or duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based clustering strategy that helps to generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision.
Errica, Federico; Podda, Marco; Bacciu, Davide; Micheli, Alessio
A Fair Comparison of Graph Neural Networks for Graph Classification Conference
Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020), 2020.
Abstract | Links | BibTeX | Tags: deep learning, deep learning for graphs, graph data, structured data processing
@conference{iclr19,
title = {A Fair Comparison of Graph Neural Networks for Graph Classification},
author = {Federico Errica and Marco Podda and Davide Bacciu and Alessio Micheli},
url = {https://openreview.net/pdf?id=HygDF6NFPB, PDF
https://iclr.cc/virtual_2020/poster_HygDF6NFPB.html, Talk
https://github.com/diningphil/gnn-comparison, Code},
year = {2020},
date = {2020-04-30},
booktitle = {Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020)},
abstract = {Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.
As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.},
keywords = {deep learning, deep learning for graphs, graph data, structured data processing},
pubstate = {published},
tppubtype = {conference}
}
Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.
As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible. Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art. To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks. Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet. We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
Podda, Marco; Micheli, Alessio; Bacciu, Davide; Milazzo, Paolo
Biochemical Pathway Robustness Prediction with Graph Neural Networks Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
BibTeX | Tags: bioinformatics, biomedical data, deep learning for graphs, structured data processing
@conference{esann20Podda,
title = { Biochemical Pathway Robustness Prediction with Graph Neural Networks },
author = {Marco Podda and Alessio Micheli and Davide Bacciu and Paolo Milazzo},
editor = {Michel Verleysen},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {bioinformatics, biomedical data, deep learning for graphs, structured data processing},
pubstate = {published},
tppubtype = {conference}
}
Errica, Federico; Bacciu, Davide; Micheli, Alessio
Theoretically Expressive and Edge-aware Graph Learning Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Abstract | Links | BibTeX | Tags: deep learning for graphs, structured data processing
@conference{esann20Errica,
title = { Theoretically Expressive and Edge-aware Graph Learning },
author = {Federico Errica and Davide Bacciu and Alessio Micheli},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2001.09005},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
abstract = {We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged. },
keywords = {deep learning for graphs, structured data processing},
pubstate = {published},
tppubtype = {conference}
}
We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged. Crecchi, Francesco; de Bodt, Cyril; Bacciu, Davide; Verleysen, Michel; John, Lee
Perplexity-free Parametric t-SNE Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
BibTeX | Tags: data visualization, manifold learning, neural networks, unsupervised learning
@conference{esann20Crecchi,
title = { Perplexity-free Parametric t-SNE},
author = {Francesco Crecchi and Cyril de Bodt and Davide Bacciu and Michel Verleysen and Lee John},
editor = {Michel Verleysen},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {data visualization, manifold learning, neural networks, unsupervised learning},
pubstate = {published},
tppubtype = {conference}
}
Bacciu, Davide; Mandic, Danilo
Tensor Decompositions in Deep Learning Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Links | BibTeX | Tags: deep learning, structured data processing, tensor factorization
@conference{esann20Tutorial,
title = {Tensor Decompositions in Deep Learning},
author = {Davide Bacciu and Danilo Mandic},
editor = {Michel Verleysen},
url = {https://arxiv.org/abs/2002.11835},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {deep learning, structured data processing, tensor factorization},
pubstate = {published},
tppubtype = {conference}
}
Castellana, Daniele; Bacciu, Davide
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data Conference
Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Links | BibTeX | Tags: deep learning, structured data processing, tensor factorization, tree structured data
@conference{esann20Castellana,
title = { Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data },
author = {Daniele Castellana and Davide Bacciu},
editor = {Michel Verleysen},
url = {https://arxiv.org/pdf/2006.10619.pdf, Arxiv},
year = {2020},
date = {2020-04-21},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20)},
keywords = {deep learning, structured data processing, tensor factorization, tree structured data},
pubstate = {published},
tppubtype = {conference}
}
Bacciu, Davide; Micheli, Alessio
Deep Learning for Graphs Book Chapter
Oneto, Luca; Navarin, Nicolo; Sperduti, Alessandro; Anguita, Davide (Ed.): Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019), 896 , pp. 99-127, Springer International Publishing, 2020, ISBN: 978-3-030-43883-8.
Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing
@inbook{graphsBDDL2020,
title = {Deep Learning for Graphs},
author = {Davide Bacciu and Alessio Micheli},
editor = {Luca Oneto and Nicolo Navarin and Alessandro Sperduti and Davide Anguita
},
url = {https://link.springer.com/chapter/10.1007/978-3-030-43883-8_5},
doi = {10.1007/978-3-030-43883-8_5},
isbn = {978-3-030-43883-8},
year = {2020},
date = {2020-04-04},
booktitle = {Recent Trends in Learning From Data: Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019)},
volume = {896},
pages = {99-127},
publisher = {Springer International Publishing},
series = {Studies in Computational Intelligence Series},
abstract = {We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.},
keywords = {deep learning for graphs, generative model, graph data, molecule generation, recurrent neural network, structured data processing},
pubstate = {published},
tppubtype = {inbook}
}
We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.
Ferrari, Elisa; Retico, Alessandra; Bacciu, Davide
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI) Journal Article
Artificial Intelligence in Medicine, 103 , 2020.
Abstract | Links | BibTeX | Tags: artificial intelligence, bioinformatics, biomedical data, explainable AI, statistics
@article{aime20Confound,
title = {Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)},
author = {Elisa Ferrari and Alessandra Retico and Davide Bacciu},
url = {https://arxiv.org/abs/1905.08871},
doi = {10.1016/j.artmed.2020.101804},
year = {2020},
date = {2020-03-01},
journal = {Artificial Intelligence in Medicine},
volume = {103},
abstract = {Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data. },
keywords = {artificial intelligence, bioinformatics, biomedical data, explainable AI, statistics},
pubstate = {published},
tppubtype = {article}
}
Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data.
2019
Bacciu, Davide; Micheli, Alessio; Podda, Marco
Edge-based sequential graph generation with recurrent neural networks Journal Article
Neurocomputing, 2019.
Abstract | Links | BibTeX | Tags: deep learning for graphs, generative model, graph data, structured data processing
@article{neucompEsann19,
title = {Edge-based sequential graph generation with recurrent neural networks},
author = {Davide Bacciu and Alessio Micheli and Marco Podda},
url = {https://arxiv.org/abs/2002.00102v1},
year = {2019},
date = {2019-12-31},
journal = {Neurocomputing},
abstract = { Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions, while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task. },
keywords = {deep learning for graphs, generative model, graph data, structured data processing},
pubstate = {published},
tppubtype = {article}
}
Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a sequential one, relying on a node ordering procedure. We use this sequential process to design a novel generative model composed of two recurrent neural networks that learn to predict the edges of graphs: the first network generates one endpoint of each edge, while the second network generates the other endpoint conditioned on the state of the first. We test our approach extensively on five different datasets, comparing with two well-known baselines coming from graph literature, and two recurrent approaches, one of which holds state of the art performances. Evaluation is conducted considering quantitative and qualitative characteristics of the generated samples. Results show that our approach is able to yield novel, and unique graphs originating from very different distributions, while retaining structural properties very similar to those in the training sample. Under the proposed evaluation framework, our approach is able to reach performances comparable to the current state of the art on the graph generation task.