320 citations · 740 across the 21 of their papers we have counts for
7 papers · 1 filter
Graph Mixture Density Networks
Federico Errica, Davide Bacciu, Alessio Micheli
We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. B…
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
Federico Errica, Marco Giulini, Davide Bacciu +3
The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless developments of computer architectures and algorithms. This explos…
Ring Reservoir Neural Networks for Graphs
Claudio Gallicchio, Alessio Micheli
Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and…
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…
Machine learning approaches for identifying prey handling activity in otariid pinnipeds
Rita Pucci, Alessio Micheli, Stefano Chessa +1
Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classifica…
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…