ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks

Anonymous

Sep 27, 2018 (modified: Oct 10, 2018) ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased and has low variance. Exploiting data augmentation, REINFORCE, and reparameterization, the ARM estimator achieves adaptive variance reduction for Monte Carlo integration by merging two expectations via common random numbers. The variance-reduction mechanism of the ARM estimator can also be attributed to antithetic sampling in an augmented space. Experimental results show the ARM estimator provides state-of-the-art performance in auto-encoding variational Bayes and maximum likelihood inference, for discrete latent variable models with one or multiple stochastic binary layers. Python code is available at https://github.com/ABC-anonymous-1.
  • Keywords: Antithetic sampling, data augmentation, deep discrete latent variable models, variance reduction, variational auto-encoder
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