Performing machine learning on structured data is compli- cated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recur- sive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convo- lutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state- of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.

Embeddings and Representation Learning for Structured Data

MICHELI, ALESSIO;Alessandro Sperduti
2019

Abstract

Performing machine learning on structured data is compli- cated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recur- sive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convo- lutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state- of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.
ESANN 2019 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
978-287-587-065-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3311042
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