Multi-Source Neural Variational Inference


  • Richard Kurle Technical University of Munich
  • Stephan Günnemann Technical University of Munich
  • Patrick van der Smagt Volkswagen Group



Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source’s posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting.




How to Cite

Kurle, R., Günnemann, S., & van der Smagt, P. (2019). Multi-Source Neural Variational Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 4114-4121.



AAAI Technical Track: Machine Learning