## Reparameterization Tricks: Variance Reduction by Smarter Gradients How moving randomness outside the computation graph turns noisy gradient estimators into low-variance, differentiable ones The reparameterization trick is what makes Variational Autoencoders (VAEs) trainable with standard stochastic gradient descent. It works by moving randomness outside the computation graph and turns an awkward gradient of an expectation into an ordinary chain-rule derivative. A VAE is a generative model. An encoder maps input data xto a distribution over a latent variable z, while a decoder maps a…