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Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling
In this paper we accelerate HMC simulations by learning atom-dependent timesteps and the number of molecular dynamics steps! With an appropriate choice of the loss, we can significantly speed up simulations and reduce autocorrelation times by 25% on alanine dipeptide.
Henrik Christiansen, Federico Errica, Francesco Alesiani
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PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs
A Python library to easily experiment with Deep Graph Networks (DGNs)
Federico Errica, Davide Bacciu, Alessio Micheli
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Towards Learning Trustworthily, Automatically, and with Guarantees on Graphs: an Overview
An overview of the current works focused towards learning trustworthily, automatically, and with guarantees on graphs.
Luca Oneto, Nicolo Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti
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Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark
We study the phenomenon of catastrophic forgetting in the graph representation learning scenario.
Antonio Carta, Andrea Cossu, Federico Errica, Davide Bacciu
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A deep graph network-enhanced sampling approach to efficiently explore the space of reduced representations of proteins
Learning complex measure of information loss in reduce representations of proteins via deep graph networks.
Federico Errica, Marco Giulini, Davide Bacciu, Roberto Menichetti, Alessio Micheli, Raffaello Potestio
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Probabilistic Learning on Graphs via Contextual Architectures
Further studies on the Contextual Graph Markov Model.
Davide Bacciu, Federico Errica, Alessio Micheli
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A Gentle Introduction to Deep Learning for Graphs
A top-down approach to the field of machine learning for graphs.
Davide Bacciu, Federico Errica, Marco Podda, Alessio Micheli
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