3 papers
stat.ML2020
A Deep Generative Model for Fragment-Based Molecule Generation
Marco Podda, Davide Bacciu, Alessio Micheli
Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in ho…
stat.ML2020
Edge-based sequential graph generation with recurrent neural networks
Davide Bacciu, Alessio Micheli, Marco Podda
Graph generation with Machine Learning is an open problem with applications in various research fields. In this work, we propose to cast the generative process of a graph into a se…
cs.LG2019
A Gentle Introduction to Deep Learning for Graphs
Davide Bacciu, Federico Errica, Alessio Micheli +1
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap inc…