AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks
Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito

Last modified: 2018-04-29

Abstract


Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been successfully employed for tractable inference. Here, we extend their scope towards unsupervised representation learning: we encode samples into continuous and categorical embeddings and show that they can also be decoded back into the original input space by leveraging MPE inference. We characterize when this Sum-Product Autoencoding (SPAE) leads to equivalent reconstructions and extend it towards dealing with missing embedding information. Our experimental results on several multi-label classification problems demonstrate that SPAE is competitive with state-of-the-art autoencoder architectures, even if the SPNs were never trained to reconstruct their inputs.

Keywords


sum-product networks, representation learning, tractable probabilistic models, unsupervised learning, autoencoders

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