Conservativeness of Untied Auto-Encoders

Authors

  • Daniel Im University of Montreal
  • Mohamed Belghazi University of Montreal
  • Roland Memisevic University of Montreal

DOI:

https://doi.org/10.1609/aaai.v30i1.10268

Keywords:

Auto-encoders, Neural Networks

Abstract

We discuss necessary and sufficient conditions for an auto-encoder to define a conservative vector field, in which case it is associated with anenergy function akin to the unnormalized log-probability of the data.We show that the conditions for conservativeness are more general than for encoder and decoder weights to be the same ("tied weights''), and thatthey also depend on the form of the hidden unit activation functions.Moreover, we show that contractive training criteria, such as denoising, enforces these conditions locally.Based on these observations, we show how we can use auto-encoders to extract the conservative component of a vector field.

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Published

2016-02-21

How to Cite

Im, D., Belghazi, M., & Memisevic, R. (2016). Conservativeness of Untied Auto-Encoders. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10268

Issue

Section

Technical Papers: Machine Learning Methods