18 citations · 82 across the 26 of their papers we have counts for
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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…
stat.ML2018
Learning Tree Distributions by Hidden Markov Models
Davide Bacciu, Daniele Castellana
Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main app…