Category Archives: research

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!).

Daniele Castellana, Davide Bacciu: Learning from Non-Binary Constituency Trees via Tensor Decomposition. PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020), 2020.

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!

Davide Bacciu, Federico Errica, Alessio Micheli: Probabilistic Learning on Graphs via Contextual Architectures. In: Journal of Machine Learning Research, vol. 21, no. 134, pp. 1−39, 2020.

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!

Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda: A Gentle Introduction to Deep Learning for Graphs. In: Neural Networks, vol. 129, pp. 203-221, 2020.

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!!

Marco Podda, Alessio Micheli, Davide Bacciu, Paolo Milazzo: Biochemical Pathway Robustness Prediction with Graph Neural Networks . Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Federico Errica, Davide Bacciu, Alessio Micheli: Theoretically Expressive and Edge-aware Graph Learning . Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Francesco Crecchi, Cyril de Bodt, Davide Bacciu, Michel Verleysen, Lee John: Perplexity-free Parametric t-SNE. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Davide Bacciu, Danilo Mandic: Tensor Decompositions in Deep Learning. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Daniele Castellana, Davide Bacciu: Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data . Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.

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..

Andrea Valenti, Antonio Carta, Davide Bacciu: Learning a Latent Space of Style-Aware Music Representations by Adversarial Autoencoders. Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), 2020.

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!

Elisa Ferrari, Alessandra Retico, Davide Bacciu: Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI). In: Artificial Intelligence in Medicine, vol. 103, 2020.

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.

Marco Podda, Davide Bacciu, Alessio Micheli: A Deep Generative Model for Fragment-Based Molecule Generation. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) , 2020.

ICLR2020 Paper

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

Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli: A Fair Comparison of Graph Neural Networks for Graph Classification. Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020), 2020.