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.

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**

# 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.

# New Ph.D. graduate

Congratulations to Francesco Crecchi that just defended his Ph.D. thesis on “Deep Learning Safety under Non-Stationarity Assumptions”, jointly supervised by me and Battista Biggio. Francesco is my first Ph.D. student to graduate, so that doubles the celebrations on my side. Kudos!!

# 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!

# Reinforcement Learning course kickoff

The new edition of the Reinforcement Learning course will kickoff on Monday 29/03/2021 h. 16.00.

This is a course offered to M.Sc. Students of the AI Curriculum (recognition as 3 Free-choice CFU) and Ph.D. students. For furher information please check the official course Moodle.

# 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

# 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..

# New M.Sc. Graduates in AI

Big graduation day in the ML family with 3 newly graduated students (two cum laude!) on topics ranging from neuro-probabilistic models for graphs, interpretability of graph neural networks for chemistry and cognitive architectures for creativity. Congratulations!

Daniele Arioli, *CognAC: a cognitive architecture based on Information Dynamics of Thinking*, co-supervised with V. Gervasi, M.Sc. in Computer Science & Artificial Intelligence

Valerio De Caro, *Graph Relative Density Networks*, M.Sc. in Computer Science & Artificial Intelligence

Danilo Numeroso, *Explaining Deep Graph Networks By Structured Counterfactual Explanations*, M.Sc. in Computer Science & Artificial Intelligence

# Paper Accepted at COLING 2020

Congratulations to Daniele Castellana for having his paper accepted at COLING 2020. Check it out if you are interested in higher-order neural networks for parse trees using tensor decompositions (soon on the Arxiv!).