Category Archives: research

H2020 TEACHING Final Review

Friday marked a very important personal milestone. The first EU project I coordinated came to an end after a fantastic final review in Athens, thanks to the spectacular TEACHING_H2020 Consortium. These have been an intense 4 years together, which nevertheless let me with the feeling and desire to sail much further with this crew. Stay tuned, as many final results of TEACHING_H2020 are coming up soon!

H2020 TEACHING Demo @BRIGHT Night 2023

All set for the Bright Night at Università di Pisa with the TEACHING_H2020 demonstrators. If you are in Pisa and want to challenge your selves with pushing a car with your mind or experience our enhanced racing simulator which can mine your drivers’ reaction with hashtag#AI come visit us at Logge di Banchi, Pisa!
Made possible by the huge effort (and recklessness) of Alessandro Berti and Valerio De Caro

Talk at Deep Learning Workshop in Trento

Here is a video of my recent talk on “Shaping Neural Networks with Dynamical Systems” at the DEEP LEARNING: Theory, Algorithms, and Applications 2023 Workshop in Trento.

In this 25min video I discuss the fundamentals of using dynamical systems’ concepts to imbue non-dissipation properties in neural networks and how we leverage dynamical systems as NNs building blocks in our EMERGE Project.

Video URL: https://drive.google.com/file/d/1RX8b-nCYqgR0sIOZLSkdrxDN35uLMG25/view

Best paper award @AAAI 2023

Great news from overseas as our paper on “Non-dissipative propagation by anti-symmetric deep graph networks” has just received the Best Student Paper Award 🏆 🍾 at the Deep Learning for Graphs workshop of AAAI23.

The paper is a great piece of work by Alessio Gravina, with a bit of support by Claudio Gallicchio and myself.

An extended version of it will s.oon be presented at ICLR 2023

New year updates

Quite a busy semester, hence long time no posts. Lets start the year with a bunch of good news.

Our book on Deep Learning in Biology and Medicine, edited with Paulo Lisboa, and Alfredo Vellido, is finally available for preorder. Check it out here!

Davide Bacciu, Paulo J. G. Lisboa, Alfredo Vellido: Deep Learning in Biology and Medicine. World Scientific Publisher, 2022, ISBN: 978-1-80061-093-4.

July 2022 is going to be a busy month in Padova with the organization of IEEE WCCI 2022, including

  • 1st Pervasive Artificial Intelligence Workshop co-chaired with Antonio Carta (Università di Pisa), Patrizio Dazzi (ISTI-CNR),  Magdalini Eirinaki (San Jose State University), Iraklis Varlamis (Harokopio University of Athens) – Deadline early April 2022
  • Deep learning for graphs special session, co-chaired with Shirui Pan (Monash University), Daniele Grattarola (IDSIA), Miao Zhang (Aalborg University), Nicolò Navarin (University of Padova), Feng Xia (Federation University Australia), Daniele Zambon (IDSIA) – Deadline 31st January 2022

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.

Neurips 2020 WS papers

Excellent result by our group in the upcoming NeurIPS 2020 workshops with four accepted papers.
Congrats to Antonio Carta, Francesco Landolfi, Danilo Numeroso and Matteo Ronchetti!

Preprints coming up..

Matteo Ronchetti, Davide Bacciu: Generative Tomography Reconstruction. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Deep Learning and Inverse Problems, 2020.

Davide Bacciu, Alessio Conte, Roberto Grossi, Francesco Landolfi, Andrea Marino: K-plex Cover Pooling for Graph Neural Networks. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020.

Davide Bacciu, Danilo Numeroso: Explaining Deep Graph Networks with Molecular Counterfactuals. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020.

Antonio Carta, Alessandro Sperduti, Davide Bacciu : Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization . 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Beyond BackPropagation: Novel Ideas for Training Neural Architectures, 2020.