The 2019/20 edition of the ISPR course will start with the first lecture on
THURSDAY 20 FEBRUARY 2020 – h. 14-16
CLASSROOM L1 – POLO FIBONACCI (First Floor)
See you there!
Big @ESANN2020
Great success at ESANN this year, with 5 papers in: congrats to Daniele, Francesco, Federico and Marco!!
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.
Theoretically Expressive and Edge-aware Graph Learning . Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Perplexity-free Parametric t-SNE. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
Tensor Decompositions in Deep Learning. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'20), 2020.
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.
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..
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!
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.
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!
A Fair Comparison of Graph Neural Networks for Graph Classification. Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020), 2020.
Neural networks on Treccani
Very proud of my little dissemination article on the Lingua Italiana magazine of Treccani. Check it out here (in Italian I am afraid)
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!
HTN code
Thanks to the invaluable work of my student Valerio De Caro, the code for the Hidden Tree Markov Network has been finally released. Check out Valerio’s github.