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Interpretable VAEs for nonlinear group factor analysis

Samuel Ainsworth,N. Foti,Adrian K. C. Lee,E. Fox

Published 2018 in International Conference on Machine Learning

ABSTRACT

Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural grouping. It is often of interest to understand systems of interaction amongst these groups, and latent factor models (LFMs) are an attractive approach. However, traditional LFMs are limited by assuming a linear correlation structure. We present an output interpretable VAE (oi-VAE) for grouped data that models complex, nonlinear latent-to-observed relationships. We combine a structured VAE comprised of group-specific generators with a sparsity-inducing prior. We demonstrate that oi-VAE yields meaningful notions of interpretability in the analysis of motion capture and MEG data. We further show that in these situations, the regularization inherent to oi-VAE can actually lead to improved generalization and learned generative processes.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Conference on Machine Learning

  • Publication date

    2018-02-17

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1802.06765
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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