Monthly Archives: January 2020

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.