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2017 | OriginalPaper | Chapter

A Network Architecture for Multi-Multi-Instance Learning

Authors : Alessandro Tibo, Paolo Frasconi, Manfred Jaeger

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

We study an extension of the multi-instance learning problem where examples are organized as nested bags of instances (e.g., a document could be represented as a bag of sentences, which in turn are bags of words). This framework can be useful in various scenarios, such as graph classification, image classification and translation-invariant pooling in convolutional neural network. In order to learn multi-multi instance data, we introduce a special neural network layer, called bag-layer, whose units aggregate sets of inputs of arbitrary size. We prove that the associated class of functions contains all Boolean functions over sets of sets of instances. We present empirical results on semi-synthetic data showing that such class of functions can be actually learned from data. We also present experiments on citation graphs datasets where our model obtains competitive results. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5442451.

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Title: A Network Architecture for Multi-Multi-Instance Learning
Authors: Alessandro Tibo
Paolo Frasconi
Manfred Jaeger
Publisher: Springer International Publishing
Book: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-319-71248-2

Electronic ISBN: 978-3-319-71249-9

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-71249-9_44

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