Category Archives: deep learning

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.

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.

Learning for graphs session @WCCI2020

I am happy to announce that I will be co-organizing a WCCI-2020 special session on Learning Representations for Structured Data together with Filippo Maria Bianchi (UIT), Thomas Gärtner (University of Nottingham), Nicolò Navarin (University of Padova) and Alessandro Sperduti (University of Padova)

Paper submission deadline is 15 January 2020 (extension pending).

Send you work through the conference submission site!

Big AI graduation day

Today we graduated a batch of 7 students from the first cycle of our AI M.Sc. of Computer science! Congratulations to my students of the AI curriculum, Andrea Cossu, Michele Cafagna, Federico Rossetto, Silvia Severini, as well as to Francesco Landolfi, from our previous M.Sc. Degree.

New accepted papers

A bunch of new papers on deep learning for graphs and neural language processing has just been accepted for publication. Check them out!

Davide Bacciu, Antonio Carta: Sequential Sentence Embeddings for Semantic Similarity. Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI'19), IEEE, 2019.

Michele Cafagna, Lorenzo De Mattei, Davide Bacciu, Malvina Nissim: Suitable doesn’t mean attractive. Human-based evaluation of automatically generated headlines. Proceedings of the 6th Italian Conference on Computational Linguistics (CLiC-it 2019), vol. 2481 , AI*IA series CEUR, 2019.

Davide Bacciu, Luigi Di Sotto: A non-negative factorization approach to node pooling in graph convolutional neural networks. Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AIIA 2019), Lecture Notes in Artificial Intelligence Springer-Verlag, 2019.