Abstract
In this chapter, we first describe what representation learning is and why we need representation learning. Among the various ways of learning representations, this chapter focuses on deep learning methods: those that are formed by the composition of multiple non-linear transformations, with the goal of resulting in more abstract and ultimately more useful representations. We summarize the representation learning techniques in different domains, focusing on the unique challenges and models for different data types including images, natural languages, speech signals and networks. Last, we summarize this chapter.
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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Zhao, L., Wu, L., Cui, P., Pei, J. (2022). Representation Learning. In: Wu, L., Cui, P., Pei, J., Zhao, L. (eds) Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-6054-2_1
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DOI: https://doi.org/10.1007/978-981-16-6054-2_1
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