Yearly Archives: 2021

ICML 2021 paper

Extremely happy and excited to share that our paper “Graph Mixture Density Networks” has been accepted for publication at ICML 2021! Huge congrats to Federico Errica for his second ICML paper and to Alessio Micheli who shares with me Federico’s supervision towards a brilliant Ph.D!

Check out the paper (soon on Arxiv in camera-ready form) to discover how we introduced first model for learning multi-modal output distributions conditioned on arbitrary graphs, and its application to epidemiology.

Federico Errica, Davide Bacciu, Alessio Micheli: Graph Mixture Density Networks. Proceedings of the 38th International Conference on Machine Learning (ICML 2021), PMLR, 2021.

IJCNN 2021 papers

Our group had 4 papers recently accepted to the upcoming International Joint Conference on Neural Networks! Much work on deep learning for graphs, including a novel edge-based model, an efficient graph generation approach and an explanation method for the chemical domain. Also a first proposal for an efficient federation of reservoir computing methods, part of our H2020 TEACHING efforts. Preprints soon on the Arxiv!

Daniele Atzeni, Davide Bacciu, Federico Errica, Alessio Micheli: Modeling Edge Features with Deep Bayesian Graph Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE IEEE, 2021.

Danilo Numeroso, Davide Bacciu: MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE 2021.

Davide Bacciu, Daniele Di Sarli, Pouria Faraji, Claudio Gallicchio, Alessio Micheli: Federated Reservoir Computing Neural Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE, 2021.

Davide Bacciu, Marco Podda: GraphGen-Redux: a Fast and Lightweight Recurrent Model for Labeled Graph Generation. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2021), IEEE 2021.

New AI Graduates!

Another big graduation day today, with plenty of contributions from our group! Congratulations to all students for their achievement. Check them out below. Pleanty of cool stuff including distillation-based continual learning, medical image analysis and generation, reinforcement learning and quantum computing as well as emotion understanding. Well done to all of you!

Apprendimento con rinforzo: un’esperienza d’uso nel gioco Asso Pigliatutto, Enrico Tomasi, Laurea in Informatica, Università di Pisa, A.A. 2019/2020

Distilled Replay: Mitigating Forgetting through Dataset Distillation (co-supervised with A. Carta, A. Cossu), Andrea Rosasco, Laurea Magistrale in Informatica (Curriculum AI), Università di Pisa, A.A. 2019/2020

ANSIA: A Neural System that Infers Affects (co-supervised with C.Gallicchio), Matteo Montalbetti, Laurea in Informatica, Università di Pisa, A.A. 2019/2020

Quantum Control via Deep Reinforcement Learning using IBMQ platform and Qiskit Pulse (co-supervision by Enrico Prati, CNR), Rudy Semola, Laurea Magistrale in Informatica (Curriculum AI), Università di Pisa, A.A. 2019/2020

Tomography reconstruction with end-to-end neural networks, Matteo Ronchetti, Laurea Magistrale in Informatica (Curriculum AI), Università di Pisa, A.A. 2019/2020

Dynamic neural networks for COVID-19 severity prediction from lung ultrasound (co-supervised with F. Faita, IFC-CNR), Ruggiero Santo, Laurea Magistrale in Informatica (Curriculum AI), Università di Pisa, A.A. 2019/2020