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!).

# Category Archives: research

# New JMLR paper

Couldn’t think of a better venue for my 99th research paper than the Journal of Machine Learning Research. Check out our work on deep and probabilistic learning for graphs. Terrific job by Federico Errica!

# Deep Learning for graph on Neural Networks

Very proud of the last effort from our group! Our tutorial paper on deep learning for graphs will be published as an invited paper on the Neural Networks journal!

Check out a preliminary version on the Arxiv!

# COVID-19 Task Force

If you work in AI and you are willing to contribute to the worldwide fight against COVID-19 please consider joining the CLAIRE-COVID19 task force. I am coordinating the workgroup on omics, chemical and clinical data processing.

You can have a look at the first result of our pan-european collaboration which has been released here: it is a curated repository of protein-viral-drug-disease interactions for helping research in drug repurposing, bio-informatics, etc.

# Big @ESANN2020

Great success at ESANN this year, with 5 papers in: congrats to Daniele, Francesco, Federico and Marco!!

# H2020 TEACHING kickoff

The H2020 project TEACHING “A computing Toolkit for building Efficient Autonomous appliCations leveraging Humanistic INtelliGence” is about to take off! Kickoff meeting in Bruxelles this week. Stay tuned…

# Amused by MusAE

Our work on MusAE, an adversarial autoencoder for music generation, has just been accepted at ECAI2020. Congratulations to Andrea for his first paper! Soon on the Arxiv..

# Artificial Intelligence in Medicine paper

Check out our newly accepted journal paper on discovering and measuring the confounding effect of data attributes in biomedical tasks. Preprint available on the Arvix. Congratulations to Elisa!

# AISTATS2020 Paper

Our paper on fragment-based molecule generation just got accepted at AISTATS 2020: congratulations to Marco Podda for the achievement! Soon on the Arxiv.

# ICLR2020 Paper

Our paper on benchmarking deep learning models for graph classification has been accepted at ICML 2020. Check it out!