Search Results for author: Federico Errica

Found 10 papers, 6 papers with code

Graph Mixture Density Networks

1 code implementation5 Dec 2020 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.

Density Estimation Graph Representation Learning

Theoretically Expressive and Edge-aware Graph Learning

no code implementations24 Jan 2020 Federico Errica, Davide Bacciu, Alessio Micheli

We propose a new Graph Neural Network that combines recent advancements in the field.

Graph Learning

A Gentle Introduction to Deep Learning for Graphs

2 code implementations29 Dec 2019 Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda

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.

Graph Representation Learning

A Fair Comparison of Graph Neural Networks for Graph Classification

1 code implementation ICLR 2020 Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli

We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

General Classification Graph Classification +2

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

1 code implementation ICML 2018 Davide Bacciu, Federico Errica, Alessio Micheli

We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.

General Classification

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