Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks
DOI:
https://doi.org/10.1609/aaai.v32i1.11734Keywords:
sum-product networks, representation learning, tractable probabilistic models, unsupervised learning, autoencodersAbstract
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.