In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables.
we have: Although this is clearly a biased estimator, it has been shown to be a fast and efficient method relative to other gradient estimators for discrete variables, especially for the Bernoulli case (Bengio et al., 2013;Hubara et al., 2016;Theis et al., 2017).